System and method implementing campaign products and services within an intelligent digital experience development platform

ABSTRACT

The present disclosure relates to digital experience development platforms and, more particularly, one or more components, systems and methods thereof of an intelligent digital experience development platform (IDXDP) configured to assist different users in the development, design and deployment of digital applications. A computer-implemented method comprises: receiving, by a computing device of a cloud-based campaign system, a campaign for products or services from a marketing system via a network; providing, by the computing device, the campaign to one or more participants on the network; obtaining, by the computing device, feedback from the one or more participants regarding the campaign; analyzing, by an artificial intelligence tool of the computing device, the feedback to generate marketing analytics information; and providing, by the computing device, the marketing analytics information to the marketing system via the network.

FIELD OF THE INVENTION

The present disclosure relates to digital experience developmentplatforms and, more particularly, one or more components, systems andmethods thereof of an intelligent digital experience developmentplatform (IDXDP) configured to assist different users in thedevelopment, design and deployment of digital applications.

BACKGROUND

Digital experience, or application, development is the process by whichapplication software is developed for handheld devices, such as personaldigital assistants, enterprise digital assistants, mobile phones (e.g.,smart phones), tablet computers, embedded devices, virtual personalassistants (VPAs), smart televisions, in-vehicle infotainment systems,appliances, etc. Digital applications (e.g., “apps”) can bepre-installed on devices during manufacturing, downloaded by customersfrom various digital software distribution platforms, or delivered asweb applications using server-side or client-side processing to providean application-like experience within a web browser.

In order to develop such apps, a developer, designer, etc. requiresseveral tools, e.g., toolsets. These tools can include, e.g., integrateddevelopment environment (IDE) tools, provided by different studios.These tools can be used to develop applications for differentapplications and operating systems. These tools can be used by thedeveloper, for example, to create or design different assets such asskins, widgets, platform specific icons, buttons, etc. To complicatematters, though, there is no standard way of creating these differentassets, leaving the developer with the need to constantly recreate suchassets without knowledge of how other developers or designers havecreated the same asset. Also, these tools only provide limitedassistance to the developer or designer, requiring the customer torequest manual support from the service provider. This tends to be atedious and expensive process.

SUMMARY

In a first aspect of the invention, there is a computer-implementedmethod for performing the steps/functionality described herein. Inanother aspect of the invention, there is a computer program productincluding a computer readable storage medium having program instructionsembodied therewith. The program instructions are executable by acomputing device to cause the computing device to perform thesteps/functionality described herein. In another aspect of theinvention, there is system including a processor, a computer readablememory, and a computer readable storage medium. The system includesprogram instructions to perform the steps/functionality describedherein. The program instructions are stored on the computer readablestorage medium for execution by the processor via the computer readablememory.

In aspects of the invention, a computer-implemented method comprises:receiving, by a computing device of a cloud-based campaign system, acampaign for products or services from a marketing system via a network;providing, by the computing device, the campaign to one or moreparticipants on the network; obtaining, by the computing device,feedback from the one or more participants regarding the campaign;analyzing, by an artificial intelligence tool of the computing device,the feedback to generate marketing analytics information; and providing,by the computing device, the marketing analytics information to themarketing system via the network.

In aspects of the invention, a computer program product comprises acomputer readable storage medium having program instructions embodiedtherewith, the program instructions executable by a computing device tocause the computing device to: determine that one or more participantsin a marketing network have opted to receive campaigns for products orservices; receive a campaign for products or services from a marketingsystem in the marketing network; provide the campaign to the one or moreparticipants on the marketing network based on the determining that theone or more participants in the marketing network opted to receivecampaigns for products or services; obtain feedback from the one or moreparticipants regarding the campaign; analyze, by an artificialintelligence tool of the computing device, the feedback to generatemarketing analytics information; and provide the marketing analyticsinformation to the marketing system.

In aspects of the invention, a campaign system comprises: a processor, acomputer readable memory and a computer readable storage mediumassociated with a computing device; program instructions to determinethat one or more participants in a marketing network have opted toreceive notifications for products or services and/or campaigns forproducts or services; program instructions to receive a notification forproducts or services and/or a campaign for products or services from amarketing system in the marketing network; program instructions toprovide the notification and/or the campaign to the one or moreparticipants on the marketing network based on the determining that theone or more participants in the marketing network opted to receive thenotifications and/or the campaigns; program instructions to obtainfeedback from the one or more participants regarding the notificationand/or the campaign; program instructions to analyze, by an artificialintelligence tool of the computing device, the feedback to generatemarketing analytics information; and program instructions to provide themarketing analytics information to the marketing system, wherein theprogram instructions are stored on the computer readable storage mediumfor execution by the processor via the computer readable memory.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in the detailed description whichfollows, in reference to the noted plurality of drawings by way ofnon-limiting examples of exemplary embodiments of the present invention.

FIG. 1A depicts a computing infrastructure in accordance with aspects ofthe present disclosure.

FIG. 1B depicts a cloud computing environment in accordance with aspectsof the present disclosure.

FIG. 1C shows an overview of a functional block diagram of the IDXDP inaccordance with aspects of the present disclosure.

FIG. 2A depicts a computing architecture in accordance with aspects ofthe present disclosure.

FIG. 2B depicts a swim lane diagram for implementing the stepsassociated with the intelligent application in accordance with aspectsof the present disclosure.

FIG. 3A illustrates several screen shots showing capabilities providedby an event queue processer in accordance with aspects of the presentdisclosure.

FIG. 3B depicts an implementation in a computing device in accordancewith aspects of the present disclosure.

FIG. 3C depicts an application programming interface (API) of adevelopment tool (e.g., Kony Visualizer® by Kony, Inc.) in accordancewith aspects of the present disclosure.

FIG. 3D depicts a computing environment between the development tool anda server system in accordance with aspects of the present disclosure.

FIG. 3E depicts components of a server system in accordance with aspectsof the present disclosure.

FIG. 3F depicts a swim lane diagram for implementing processes inaccordance with aspects of the present disclosure.

FIGS. 4A and 4B illustrate screen shots of recommendations for users inaccordance with aspects of the present disclosure.

FIG. 4C shows a swim lane diagram illustrating exemplary processes inaccordance with aspects of the present disclosure.

FIGS. 4D, 4E, and 4F illustrate providing autonomous recommendations toautomatically complete actions in accordance with aspects of the presentdisclosure.

FIG. 4G shows an illustrative environment in accordance with aspects ofthe present disclosure.

FIG. 4H shows a flowchart of exemplary processes for conversational botapp developing in accordance with aspects of the present disclosure.

FIG. 5A shows an exemplary computing environment in accordance withaspects of the present disclosure.

FIG. 5B shows an exemplary user interface illustrating a function inaccordance with aspects of the present disclosure.

FIG. 5C shows a flowchart of steps of exemplary processes in accordancewith aspects of the present disclosure.

FIG. 5D shows a swim lane diagram of exemplary processes in accordancewith aspects of the present disclosure.

FIG. 6A shows a flowchart of exemplary processes in accordance withaspects of the present disclosure.

FIG. 6B shows a flowchart of exemplary processes in accordance withaspects of the present disclosure.

FIG. 6C shows a swim lane diagram of exemplary processes in accordancewith aspects of the present disclosure.

FIG. 7A shows a system level or architectural overview of an interactivetutorial platform in accordance with aspects of the present disclosure.

FIG. 7B shows an exemplary display with an interactive tutorial usingthe interactive tutorial platform in accordance with aspects of thepresent disclosure.

FIG. 7C depicts a swim lane diagram for implementing steps associatedwith the interactive tutorial platform in accordance with aspects of thepresent disclosure.

FIG. 8A shows an administrative portal for providing rules in areal-time collaboration environment in accordance with aspects of thepresent disclosure.

FIG. 8B depicts a swim lane diagram for implementing steps associatedwith the administrative portal to provide rules for real-timecollaboration in accordance with aspects of the present disclosure.

FIG. 9A shows an interface for communicating to business systems (e.g.,chatbot) in accordance with aspects of the present disclosure.

FIG. 9B depicts a swim lane diagram for implementing steps associatedwith the communication with the business systems in accordance withaspects of the present disclosure.

FIG. 10A depicts a swim lane diagram for implementing steps associatedwith a registration system in accordance with aspects of the presentdisclosure.

FIG. 11A depicts a swim lane diagram for implementing steps associatedwith a marketing system in accordance with aspects of the presentdisclosure.

FIG. 12A depicts a flow diagram for implementing steps associated withthe system and method of translation application in accordance withaspects of the present disclosure.

FIG. 12B shows an architecture of system and method of the translationapplication in accordance with aspects of the present disclosure.

FIG. 12C shows screenshots before and after translation conversionsupport as implemented by the system and method of translationapplication.

FIG. 12D shows screenshots of translation of strings on forms (fromEnglish to Mandarin) as implemented by the system and method oftranslation application.

FIG. 12E shows screenshots of translation of fillable fields and statictext on forms (from English to Mandarin) as implemented by the systemand method of translation application.

DETAILED DESCRIPTION

The present disclosure relates to digital experience (also referred toas a digital application) development platforms and, more particularly,to an intelligent digital experience development platform (IDXDP)configured to assist different users in the development, design anddeployment of digital applications. The term “digital applicationdevelopment platform” or the use of the term “mobile” should not beconsidered a limiting feature as the scope of the invention is extendedto more than cellular telephones, tablets, etc. In embodiments, theplatform is extended to produce runtimes for virtual personal assistants(VPA's) (e.g. Alexa® of Amazon Technologies, Inc.), in-vehicleinfotainment systems, appliances, etc., beyond mobile. The “digitalexperience” refers more broadly to areas of enablement, which may not bepackaged as a traditional “app” or binary. It broadens the focus toinclude areas like smart television “apps”, appliance-based apps whichmay be apps or widgets, VPA type services (Alexa® by AmazonTechnologies, Inc., Google Home™, Homepod® by Apple Inc.), etc. Sogenerally, IDXDP is a broader aperture for how consumers, enterpriseemployees, etc. will “experience” technology in the future.

In embodiments, the IDXDP includes a number of discrete, intelligentplatforms and/or functionality that constitute a disruptive shift in thecapabilities of an application development platform. More specifically,the IDXDP incorporates artificial intelligence (AI) capabilities, e.g.,natural language processing (NLP), machine learning processing, andrules engines, with the integration of other functionality to provide ahighly granular event queue within an application platform to assist inthe development, design and deployment, etc., of digital applications.Advantageously, the AI capabilities will assist different types of users(personas), e.g., digital application developers, digital applicationdesigners and enterprise administrators, etc., to improve and assist inthe efficient development, design and deployment lifecycle of a digitalapplication.

For example, in embodiments, the IDXDP mines individual actions andaggregates actions on a global user basis to determine how best tosuggest methods to achieve an improved outcome. The outcome can rangefrom the development of a digital application, to the design (e.g., lookand feel) of the digital application, to the deployment of the digitalapplication within an enterprise, etc. In embodiments, the IDXDP canalso interface directly with any third party services, e.g.,Salesforce®, to assist in the upsell or other potential actions toimprove the performance and/or functionality of the digital application.

In addition, the IDXDP can be integrated with different applicationplatform such as development platforms or development tools orcomponents thereof, e.g., Kony Visualizer® by Kony, Inc.′ to identifydeficiencies within the development tool, to create its own descriptiveepics (i.e., large bodies of work that can be broken down into a numberof smaller tasks (called stories) using, e.g., project management tools,debugging tools, issue tracking tools, etc. (such as Jira® by AtlassianPty Ltd), which can then be fed directly to a development or design teamto enhance and/or improve the development life cycle, e.g., programming,functionality, testing, etc., of the digital application. Morespecifically, the IDXDP described herein can be an advanced feature thatessentially lives within an application platform, e.g., Kony Visualizer®by Kony, Inc.

It should be understood that the term object, widget, form, template,etc. are used generically herein and, in appropriate instances, each canbe interchangeable in implementing the functionality herein. Forexample, learning of a widget can be equally applicable of learning anobject, template or other asset, for the AI to make recommendations forbest practices, dead-ending purposes, creating tutorials, upselling,standardizing code, or other functionality described herein with respectto the different modules, components, tools, etc. In addition, the termasset and/or object may be understood to be the most generic terminologyfor any creation by the user in the development tool, e.g., skin,widget, template, button, etc.; although each of these terms as notedabove may be used interchangeably.

System Environment

The IDXDP may be embodied as a system, method or computer programproduct. The IDXDP may take the form of a hardware embodiment, asoftware embodiment or a combination of software and hardware.Furthermore, the IDXDP may take the form of a computer program productembodied in any tangible storage medium having computer-usable programcode embodied in computer readable storage medium or device. The IDXDPoperates by processing user actions, without dependence on a user inputwidget, or can expose one or more user input methods in which a user caninvoke intelligent capabilities. User input methods can be textoriented, speech oriented, or by any other input method as is known tothose of skill in the art.

The computer readable storage medium is not a transitory signal per se,and is any tangible medium that contains and stores the program for useby or in connection with an instruction execution system, apparatus, ordevice. For example, the computer readable storage medium can compriseelectronic, magnetic, optical, electromagnetic, infrared, and/orsemiconductor systems and/or devices. More specific examples (anon-exhaustive list) of the computer readable storage medium include: aportable computer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, a portable compact disc read-onlymemory (CD-ROM), an optical storage device, a magnetic storage device,or any combination thereof. Accordingly, the computer readable storagemedium may be any tangible medium that can contain or store a programfor use by or in connection with an instruction execution system,apparatus, or device of the present invention.

The computer readable storage medium can be used in the illustrativeenvironment 1000 shown in FIG. 1A for assisting and/or managing thedevelopment, design, deployment and/or other enhancements orfunctionality of any number of digital applications. In embodiments, thecomputing environment 1000 includes a server 1012 or other computingsystem, which can be any platform used for the development, design ordeployment of digital applications. For example, server 1012 can berepresentative of a development tool, e.g., Kony Visualizer® by Kony,Inc. integrated within the Kony MobileFabric® tool by Kony, Inc. orother development tools or platforms for the development, design anddeployment of digital applications. The server 1012 can also berepresentative of a digital backend as a service (MBaaS), maintained bya service provider, e.g., Kony® by Kony, Inc. As should be understood bythose of ordinary skill in the art, the MBaaS is a model for providingweb and digital application developers with a way to link theirapplications to backend cloud storage and Application Program Interfaces(APIs) exposed by backend applications, while providing features such asuser management and integration.

In embodiments, the server 1012 can be a single, open standards-baseddigital infrastructure platform. The single, open standards-baseddigital infrastructure platform can unify multipleinfrastructures/platforms together, including mobile applicationdevelopment platform (MADP), mobile backend as a service (MBaaS), APImanagement, and platform as-a-service (PaaS) infrastructures. To thisend, for example, the server 1012 can be representative of KonyMobileFabric® by Kony, Inc. which is a converged mobile/digitalinfrastructure that empowers enterprises to significantly reduce time tomarket, while providing an interactive guide of best practices usingintelligent subsystems, e.g., bots, etc. In this example, the server1012 can further integrate Enterprise Mobility Management/MobileApplication Management (EMM/MAM) server functions (e.g., managementinstances), as well as incorporate any number of enterprise stores,e.g., an app store.

The server 1012 and/or processes performed by the server 1012 can beintegrated into the networking environment (e.g., cloud environment)such as shown in FIG. 1B or any enterprise management system,development tool or other system also represented in FIG. 1B. The server1012 can also access and mine the worldwide web or other externalcomputing systems 1045 for best practices in the development, design,deployment, etc., of digital applications using the intelligent systemsdescribed herein.

Still referring to FIG. 1A, the server 1012 includes a computing device1014, which can be resident on a network infrastructure or computingdevice. The computing device 1014 includes a processor 1020 (e.g., acentral processing unit (CPU)), a memory 1022A, an Input/Output (I/O)interface 1024, and a bus 1026. The bus 1026 provides a communicationslink between each of the components in the computing device 1014. Inaddition, the computing device 1014 includes a random access memory(RAM), a read-only memory (ROM), and an operating system (O/S). Thecomputing device 1014 is in communication with the external I/Odevice/resource 1028 and a storage system 1022B. The I/O device 1028 cancomprise any device that enables an individual to interact with thecomputing device 1014 (e.g., user interface) or any device that enablesthe computing device 1014 to communicate with one or more othercomputing devices (e.g., devices 1045, etc.) using any type ofcommunications link.

The processor 1020 executes computer program code (e.g., program control1044), which can be stored in the memory 1022A and/or storage system1022B. In embodiments, the program control 1044 of the computing device1014 of the server 1012 controls the tool(s) (e.g., modules) 1050described herein which can be program modules, etc., comprising programcode adapted to perform one or more of the processes described herein.The program code can include computer program instructions stored in acomputer readable storage medium. The computer program instructions mayalso be loaded onto the computing device 1014, other programmable dataprocessing apparatus, or other devices to cause a series of operationalsteps to be performed on the computer.

The tools 1050 can be implemented as one or more program code in theprogram control 1044 stored in memory 1022A as separate or combinedmodules. Additionally or alternatively, the tools 1050 may beimplemented as separate dedicated special use processors or a single orseveral processors to provide the functionality described herein. Whileexecuting the computer program code, the processor 1020 can read and/orwrite data to/from memory 1022A, storage system 1022B, and/or I/Ointerface 1024. In this manner, the program code executes the processesof the invention. By way of non-limiting illustrative example, thetool(s) (e.g., modules) 1050 are representative of the followingfunctionality of systems and methods associated with a development tool,e.g., Kony Visualizer® by Kony, Inc.

Intelligent Mobile Application Development Platform

The IDXDP can represent complex widgets, object services or othercomponents created within an integrated development environment (IDE).The IDE is a software application that provides comprehensive facilitiesto computer programmers for software development. An IDE normallyincludes of a source code editor, build automation tools, and adebugger. In more specific embodiments, the IDXDP facilitates a systemand method for representing objects by implementing a granular eventprocess, and representing user actions, object component settings, fieldvalues and other metadata. That is, the IDXDP lays a foundational eventqueue, which can be used with the additional functionality describedherein.

In embodiments, the event oriented representation and syntax provided inthe IDXDP captures a full fidelity and accurate depiction of a widget,service or other software component (known generally as an “asset”). Itsstructure can be used as a content feed for AI, machine learning andanalytics based engines (or other cognitive platforms) to determine howdevelopers and other users are constructing components and ultimatelydeveloping and/or designing digital applications. The AI and machinelearning are able to acquire insight into programming techniques todetermine how to improve design and development practices andtechniques.

In embodiments, the system and method implements a highly IDXDP capableof representing complex widgets, object services or other componentscreated within an IDE. This event queue serves as a rolling record ofthe events, which go into creating a component of a digital application.By monitoring this event queue (or stream), the IDXDP is able todetermine the intent of the developer, which can be later used toprovide recommendations of best practices for building components,tutorials or other functionality described herein. Several relatedactions can be invoked as a result of this monitoring process.

The IDXDP also lays the foundation for further functionality describedherein which allows AI (e.g., cognitive computing) to suggest betterways to achieve an outcome (e.g., building assets, service definition,etc.). The IDXDP does this by relating current actions to outcomes,which already exist, either in the developer's local workspace, asoftware marketplace or other repository.

Event Processing System and Method

The event processing system and method is a system and method forprocessing an event queue comprised of IDE user actions and constructingan object or service from the data within that queue. For example, theevent processing system and method uses the event queue as describedherein to drive recommendations to developers, designers, and otherpersonas using the IDXDP described herein. In embodiments, whenimplementing the event processing system and method, natural languagenarrations can be dynamically formed and rendered to the user to createa depth of understanding related to the component or object compositionand capabilities or other assets. In addition, a machine learningcomponent continuously optimizes design assets and offers the latestembodiment to the user.

Autonomous Advisor

The autonomous advisor is a system and method for analyzing an eventqueue to derive repetitive actions and refactor those actions intospecific application components. These application components caninclude but are not limited to forms, master templates (masters) ormacros, widgets, services, or other assets, etc. By way of example, theautonomous advisor creates a cognitive development environment capableof learning from a single user or multiple users, and creates suggestedactions based on a global community of users. The autonomous advisor isalso capable of determining the class of user (e.g., designer,developer, citizen developer, business stakeholder, etc.) and adaptingthe guidance and suggestions to the particular competency level of theclass of user.

Systems and Methods of Converting Actions Based on Determined Personas(Users)

The systems and methods of converting actions based on determinedpersonas (users) captures and aggregates multi-role user actions (e.g.,designers, developers, etc.) performed within a platform into a datarepository. The systems and methods of converting actions based ondetermined personas (users) will also perform analytics capable ofdetermining future enhancements needed to that platform. Theseenhancements will be used to more efficiently assist a user (e.g.,designers, developers, etc.) in the development, design and/ordeployment of an application. Systems and methods within the IDXDPeliminate the need for a user of the intelligent digital experiencesdevelopment platform to specify their persona of architect, designer,developer, integrator, or other persona. The intelligent digitalexperiences development platform accomplishes this by examining theuser's actions via the users Individual Developer Stream (IDS) andcomparing that with the list of actions from a Team Developer Stream(TDS) coming from users with well-known or algorithmically derivedpersonas. This enables the intelligent digital experiences developmentplatform to infer persona in cases where the user has notself-identified. This also allows the intelligent digital experiencesdevelopment platform to support Dynamic Persona Shifting (DPS).

DPS applies to users that work across multiple personas when working ontasks, but within a specific task, they are almost always acting withina single persona. DPS enables the intelligent digital experiencesdevelopment platform to identify a user's current persona based on theindividual developer stream and personalize the experience for that userfor their current task.

Additionally, an End User Usage Stream (EUS) is client side(application) functionality, which optionally collects the eventactivity stream for end-users of applications or other digitalexperiences. The EUS helps the application developer by tracking highusage features, slow functions/services, areas of positive or negativefeedback, and alerts users of the intelligent digital experiencesdevelopment platform of the user experience associated with theircurrent code or the impact of their changes in a particular component ormodule. The Autonomous Advisor (AA) acts on a single user, but alsoacross the entire team or global community to identifyduplicate/repetitive tasks. The AA or Autonomous Advisor Module (AAM)reduces rework through repetition elimination by scanning across IDS,TDS and clinical decision support (CDS) repositories, identifyingsimilar components, code snippets, services and other assets. The AAM,for example, can act on a single user, but also across the entire teamto identify duplicate/repetitive tasks across the team or fullcommunity. The AAM consumes IDS, TDS and CDS, analyzing and routing datato the Autonomous Advisor and other end points.

System and Method of Generating Actionable Intelligence Based onPlatform and Community Originated Data

The system and method of generating actionable intelligence based onplatform and community originated data is a system and method foraggregating platform and community originated tasks into events andfurther evaluating these events into actionable sales intelligence. Forexample, the system and method creates an AI capability, whichdetermines if the usage (either by a single person or by an enterprisegroup) suggests that a further sales action should be taken, e.g., anupsell for instance.

Interactive Tutorial Platform and Related Processes

The interactive tutorial platform is a system and method for the dynamicconversion of a software component, comprised of data and meta-data,into a real-time playback of component creation. This system providesthe capability to use components within a component library ormarketplace, to educate a designer, developer or other associated skillsrole in the functionality, the runtime characteristics and the end toend integration of a software component.

In embodiments, the interactive tutorial platform and related processesallows for components to tell a story about themselves. In conventionalsystems and methods, a story was often told via self-help videos, whichshows a user manually walking through the relevant steps of thedevelopment and/or design process. The self-help video is anon-interactive video, which simply shows a user going through creationsteps. In contrast, the interactive tutorial platform and relatedprocesses shows each step of its creation and behaves similar to anotherdeveloper or designer taking the user through the creation of thecomponent. For example, the methods and intelligence of developing ordesigning a digital application or component thereof, for example fromKony Marketplace™ by Kony, Inc., can now be imported into the workspaceof development tool, e.g., Kony Visualizer by Kony, Inc., andsubsequently “played”, resulting in the component (or service) informingthe user how it was actually created. This includes optional narrationtags, which can specifically be rendered as balloon text or spokenwords, thereby essentially performing a dynamic technology transfer tothe user. In embodiments, a system allowing for ratings to be associatedwith interactive renderings is also contemplated herein.

System and Method of Real-Time Collaboration

The system and method of real-time collaboration is a system and methodfor real-time collaboration between developers (or other personas)within the enterprise, and also connecting with a community ofdevelopers. By implementing the system and method of real-timecollaboration, system administrators can define collaboration rulesacross the enterprise of users or at the developer level, for example.

System and Method for Connecting End Users to Business systems

The system and method for connecting end users to business systems is asystem and method, which enhances service and generates business leads.In embodiments, the system will allow the users with certain roles(e.g., managers, lead developers, etc.) to request help for professionalservices, buy products, proposals, etc., through a chat interface. Inembodiments, the system and method for connecting end users to businesssystems routes these requests to customer relation management systemsand/or through the business systems, which logs the requests. The systemwill also autonomously respond back to the users when the previouslysent requests are updated by the interested parties. Though a chatinterface is referred to previously, this intelligent system alsofacilitates communication through other service calls or servicesintegrations to third party software components and applications.

Registration System

The registration system is a system and method, which allows vendors toregister themselves using an administrative portal/website. Inembodiments, the system and method allows third party vendors to plug inthrough the administrative portal, which will assign each vendor aunique handle. This unique handle is used by the end users tocommunicate with external vendors through the systems described herein.In embodiments, the system and methods can define the communicationcontract for the external vendors.

System and Method to Campaign Products and Services

The system and method to campaign (e.g., identify and sell) products andservices have the ability to run marketing campaigns forproduct/professional services with a targeted audience. The system andmethod provides the user with an option for the user to turn on/offcampaigns, record and submit the results of the campaign to a centralrepository, and run analytics on the response received from thecampaigns.

Cloud Computing Platform

FIG. 1B shows an exemplary cloud computing environment 1100 which can beused to implement the IDXDP. The cloud computing is a computing modelthat enables convenient, on-demand network access to a shared pool ofconfigurable computing resources, e.g., networks, servers, processing,storage, applications, and services, that can be provisioned andreleased rapidly, dynamically, and with minimal management effortsand/or interaction with a service provider. In embodiments, one or moreaspects, functions and/or processes described herein may be performedand/or provided via cloud computing environment 1100.

As shown, cloud computing environment 1100 comprises one or more cloudcomputing nodes 1110 with which local computing devices are used by thedeveloper, design, administrator of an enterprise, etc. It is understoodthat the types of computing devices 1115A, 1115B shown in FIG. 1B areintended to be illustrative only and that the cloud computing nodes 1110and the cloud computing environment 1100 can communicate with any typeof computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser). These localcomputing devices can include, e.g., desktop computer 1115A, laptopcomputer 1115B, and/or other computer system. In embodiments, the cloudcomputing nodes 1110 may communicate with one another. In addition, thecloud computing nodes 1110 may be grouped (not shown) physically orvirtually, in one or more networks, such as Private, Community, Public,or Hybrid clouds or a combination thereof. This allows cloud computingenvironment 1100 to offer infrastructure, platforms and/or software asservices for which a cloud consumer does not need to maintain resourceson a local computing device.

The cloud computing nodes 1110 can include a variety of hardware and/orsoftware computing resources, such as servers, databases, storage,networks, applications, and platforms as shown, for example, in FIG. 1A.The cloud resources 1110 may be on a single network or a distributednetwork across multiple cloud computing systems and/or individualnetwork enabled computing devices. The cloud computing nodes 1100 may beconfigured such that cloud resources provide computing resources toclient devices through a variety of service models, such as Software asa Service (SaaS), Platforms as a service (PaaS), Infrastructure as aService (IaaS), and/or any other cloud service models. The cloudcomputing nodes 1110 may be configured, in some cases, to providemultiple service models to a client device. For example, the cloudcomputing nodes 1110 can provide both SaaS and IaaS to a client device.

Functional Block Diagram for Implementing Features of the IntelligentMobile Application Development Platform

FIG. 1C shows an overview of a functional block diagram of the IDXDP(also referred to as Kony IQ™ by Kony, Inc.) in accordance with aspectsof the present disclosure. The IDXDP includes several modules asdescribed below, which are provided in a cognitive computing environmentfor omni-channel platforms. The IDXDP implements the different AI andnatural language learning components described herein to provide thedifferent functionality as described throughout the disclosure, e.g.,recommendations of best practices, tutorials, upselling actions, etc. Asshould be understood by those of skill in the art, omni-channel is amultichannel approach to sales that seeks to provide the customer with aseamless experience. Cognitive computing involves self-learning systemsthat use data mining, pattern recognition and NLP. The goal of cognitivecomputing is to create automated information technology (IT) systemsthat are capable of solving problems without requiring human assistanceor intervention.

In embodiments, the functional blocks described herein can beimplemented in the computing environment 1000 of FIG. 1A and/or theexemplary cloud computing environment 1100 of FIG. 1B. It should beunderstood that each block (and/or flowchart and/or swim laneillustration) can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts or carry outcombinations of special purpose hardware and computer instructions.Moreover, the block diagram(s), flowcharts and swim lane illustrationsdescribed herein illustrate the architecture, functionality, andoperation of possible implementations of systems, methods, and computerprogram products according to various embodiments of the presentdisclosure. In this regard, each block in the flowchart or step in theswim lane illustrations or block diagrams may represent a module,segment, or portion of instructions, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s).

Interactive Advisor Module

The interactive advisor module 1205 is configured to answer questions ofany particular user, whether it be the developer, designer,administrator, etc. So, by implementing the interactive advisor module1205, the user has the ability ask questions, e.g., “do you have anylogin templates?”, “can you clean up my project?”, “can you deduplicateor can you eliminate unused skills or unused actions?”, etc., and thesequestions will, in turn, invoke search capabilities which will returnpertinent answers within an interactive chat window. In this way, theinteractive advisor module 1205 allows for multi-persona from across anumber of technical backgrounds or even non-technical backgrounds to usethe platform. This provides the ability to interact with and optimizethe advice and how it is given to any one of those personas. Thefunctionality also includes an opted in/opted out capability.

The interactive advisor module 1205 determines the actions of the user,e.g., whether or not a user is creating a form (or other asset) in aworkspace or is collaborating with others to create such a form, anddetermines that such actions are similar or the same as another user whoalready successfully created the form (or other asset) using its machinelearning perspective. The IDXDP can then make suggestions to completethe tasks, giving examples of things that can speed up the development.These provided examples might be further along than the current user,which can then be implemented immediately by the user from a backendconductivity point right away.

Autonomous Advisor Module

The autonomous advisor module 1210 allows the IDXDP to go from aninteractive advisor to an autonomous advisor, which functions in thebackground to provide guidance to the user. For example, the autonomousadvisor module 1210 continuously looks for things, e.g., completion of acertain activity, where the user is creating a certain asset form in adigital application that represents a product list, a scrollable productlist, etc. The autonomous advisor module 1210 can then manage the staticassets using labels, image holders, etc. In embodiments, the autonomousadvisor module 1210 can be implemented to perform other functionsincluding providing recommendations of best practices, generating andproviding tutorials, etc., in addition to the other capabilitiesdescribed herein.

Another example of implementation of the autonomous advisor module 1210is a designer using color palettes. In this example, a designer may beusing particular colors to represent particular buttons, particularbackgrounds, and forms, etc. The autonomous advisor module 1210 is ableto make recommendations so that the designer stays within a particularapproved color palette in order to comply with specific guidelines orbest practices, in general. This allows the outcome from the designer tolook and feel consistent.

Global Developer Trends Module

The global developer trends module 1215 is configured to obtain globaltrends within any particular industry. For example, in implementation,the global developer trends module 1215 is not simply obtainingintelligence from one person, but on a global basis. This globalknowledge may be obtained through other applications within the platformor from external systems including the scraping of the internet or otherthird party applications, i.e., global developer pool. These globaltrends, in essence, are provided from a developer population across theglobe and then applied locally. These trends can then be used to improvethe outcome locally in the design, development and/or deployment of theapplication. These trends can include, e.g., how to use specific code ora highly customized code in a particular application.

With this noted, the global developer trends module 1215 is configuredto gather information from a global population of developers/designer inreal time using machine learning to distill their particular actionsinto consequences. Also, the global developer trends module 1215 canrelate what is found, e.g., what they are doing and how they did it, tothen implement such new techniques, etc. into the productivity of thecurrent user.

Development Advisor Module

The development operations (DevOps) advisor module 1220 is configured toprovide the ability to not just look at the tools that are available,e.g., tools that are currently in the developer kit, but a global set oftools for the development, design and deployment of the application. Inother words, the DevOps advisor module 1220 can include embedded textediting, program editing features, built panels, and variousconfigurability to assist the user in their particular tasks. Morespecifically, the DevOps advisor module 1220 provides the ability tolook at what developers are using as peripheral utilities and peripheralfeatures around the application platform, e.g., Kony Visualizer® byKony, Inc., so that when asked a question, or autonomously, the DevOpsAdvisor 1220 can recommend different tools to improve efficiency of theparticular user, e.g., developer, designer, etc. The recommendation canbe from sampling a broad array of development environments and inreal-time keeping up with these environments.

Technical Eminence Module

The technical eminence module 1225 is an overhead task most of the time,e.g., packaging an asset for marketplace, which can be made part of themarketplace. For example, the technical eminence module 1225 isconfigured to provide an enterprise or team to share assets back andforth. These assets can be shared between private and publicmarketplaces, to publicly or privately accessible repositories (e.g.,Stack Overflow® by Stack Exchange, Inc., GitHub® by GitHub, Inc. orothers), as well as amongst teams or people with a team setting, asexamples. In embodiments, the technical eminence module 1225 providesconfigurations and administrative capabilities that allow a team lead,for example, of a particular group of technical resources to usedifferent resources within the constraints of a large-scale enterprise.

To enforce particular authentication routines, e.g., a particular lookand feel of a widget that is being used/shared, the IDXDP, e.g., thetechnical eminence module 1225, can be the orchestrator between theasset just created on the public marketplace or private marketplace,stack overflow, or other kind of publication site. This is verybeneficial to the enterprise, in general, so that the user can implementreusable assets that are essentially approvable and usable, with theapproval or preauthorization from the enterprise administrator.

Plug-in Architecture Module

The plug-in architecture module 1230 is configured to incorporatemultiple “cognitive brains” into the flow of processing. In essence, theplug-in architecture module 1230 allows plug in capability of differentbots, e.g., a meta-bot, chat bots, and other tools provided herein(e.g., each of the different discrete components, systems and methodsdescribed herein such as any of the engines described herein, e.g.,recommendation engine, autonomous advisers, managers (event manager),modules of FIG. 1C, etc.) and provides the orchestration of suchplug-ins. In this way, the plug-in architecture module 1230 allows theIDXDP to be enhanceable and extendable over time.

In embodiments, the plug-in architecture module 1230 allows multiplebrains (e.g., bots having certain AI) that can be configured in such away that the handoff between those can be orchestrated and orderable,etc. In this way, it is now possible to chain intelligence, e.g., bots,from boutique level or industry-specific or enterprise specificdevelopment tools to the IDXDP to take advantage of their intelligence.This will allow cross team intelligence, which a particular brain mayoffer in order to benefit from the utility for a particular need.

The different developer or utility specific bots, e.g., AI, may then behosted locally on the development platform, e.g., Kony Visualizer® byKony, Inc. For example, there can be many different AI including aspecific type of DevOps brain that is written around the use of aparticular tool that has just emerged. Or, there may be a particularsecurity brain that is making the developer aware of particular securityparadigms when they build their digital application.

Training/Support Optimization Module

The training/support optimization module 1235 is configured to createand view an interactive type of tutorial for training a user in theconstruction or use of different assets, e.g., widgets, microapplications, mobile and digital applications, etc. These differentassets can be constructed internally as objects and name values andpairs, etc., or externally. For example, the training/supportoptimization module 1235 provides the ability to assemble an object(asset) in real-time using the IDE so that the object can actually beconstructed by the user.

More specifically, in embodiments, the training/support optimizationmodule 1235 creates a new way of creating an interpreter for theapplication platform that will allow the developer, designer or otheruser to see how something is being constructed, in real time. Thisinstruction set is provided within the context of the IDE. Also, in thecontext of the training/support optimization module 1235, an editorfunction allows the user to edit and create different content, i.e.,building of a particular asset such as a skin, widget, button,attributes or objects, or other assets, etc., with natural pauses in theplayback of that particular asset on the IDE.

The training/support optimization module 1235 also supports theinjection of speech or graphics or pop-up bubble text as examples. Thetraining/support optimization module 1235 also provides the ability toinject metadata around a particular asset so that it is now possible toconvey messages to a person that is looking at the real-time renderingof that asset within the IDE.

The support optimization portion of the training/support optimizationmodule 1235 is also configured to assist the developer in a path of bestpractices, versus just an infinite amount of re-creating of an assetover again. In addition, the support optimization portion comes in termsof data mining and machine learning where developers or other users(e.g., designers) are dead-ending in their development processes orgiving up on a certain approach. In these scenarios, the supportoptimization portion of the training/support optimization module 1235can capture and catalog the approach and provide alternativesuggestions, i.e., provide appropriate autonomous advice to continuetheir project. Accordingly, the support optimization provides a type oftrouble ticket avoidance approach. The functionality within thetraining/support optimization module also provides users with theability to record and annotate personally developed assets.

Improve Sales Efficiency Module

The improve sales efficiency module 1240 is configured to identify anuptake in use of the application platform within a certain class ofindividual or a certain enterprise, etc. In these cases, an intelligencequotient (IQ) engine (e.g., Kony IQ™ by Kony, Inc.) of the improve salesefficiency module 1240 has the ability to feedback these kind of metricsinto a salesforce service or a salesforce database which, in turn, isprovided to a member of a sales support team informing them of potentialupsell opportunities. Accordingly, the improve sales efficiency module1240 can identify and alert the user to particular upselling outside ofthe enterprise. In addition, the improve sales efficiency module 1240can identify specific upgrades to service levels directly within theapplication platform, e.g., Kony Visualizer® by Kony, Inc.

Product Evolution Module

The product evolution module 1245 is configured to keep track of featurerequests, and how to make enhancements to the application platform.Often times, enhancements to an application platform are based oncustomer feedback, written customer feedback, emails, etc. This feedbackis now to be automated, broken down into epics and different DevOpsactionable entities, to create an engineering effort around suchsuggestions. These functions also include the ability for theintelligent digital experience platform to suggest to users that theyenhance a given widget or module as it would then include extendedfunctions found in feature requests or epic repositories, making theirefforts more universally or broadly applicable.

For example, the product evolution module 1245 is configured todetermine when a developer or other users (e.g., designers) aredead-ending in their development processes or giving up on a certainapproach and dynamically creating a story (e.g., using Jira® software).In this way, there is no guessing as to the needs of the customer and,instead, the product evolution module 1245 can identify in real-time aparticular problem in order to then evolve the product to address thatparticular issue. In addition, a weighting can be provided to theproblem based on the amount of times such problem has been encountered,and placed in a prioritized queue of a project engineering based on suchweighting. This will allow the project engineering the ability toprioritize the engineering of certain problems.

In addition, the product evolution module 1245 is used to provideupdates to certain requirements or issues or other types of updates. Forexample, when a new patch has been issued for a piece of softwareproduct, a story can be automatically generated, e.g., “Please update acomponent in your application platform” and sent to the appropriatepersonas (e.g., developer, designer, etc.). As another example, theproduct evolution module 1245 is configured to notice when a samecomponent keeps causing problems such that such problem can be handed tothe analytics engine (e.g., AI) for automatically putting together afix. In this scenario, the fix is automatically targeted to those usersfor those stories, letting them know that there is a fix. Inembodiments, Jira® software can open a story to fix such a problem.

In another scenario, the product evolution module 1245 can detect that acustomer (e.g., user) has created a solution to a problem. So, if thesame error or problem is seen numerous times, the product evolutionmodule 1245 can automatically alert other users to the solution.Alternatively or in addition, the product evolution module 1245 canautomatically open a support ticket on behalf of customers, with thebackend service provider, e.g., Kony® by Kony, Inc., providing a fix tothe problem, taking it to the internal story so that the productevolution module 1245 knows that this fix goes to particular clientsthat had the particular problem.

In embodiments, the product evolution module 1245 (as with any of thecomponents or modules described herein) can be a social listener,essentially data-mining a particular operating system (OS) that may havea deprecating a function (or to find other problems and solutions in theEthernet that were encountered by a particular type of client). In thiscase, the product evolution module 1245 might trigger a different way ofperforming a task by the user, e.g., developer, or it may necessitate aplatform change by a third party service provider, e.g., Kony® by Kony,Inc., making sure that the developer can rebuild using an alternativeprocess or when a product feature may need updating within theapplication platform.

Intelligent Mobile Application Development Platform and RelatedProcesses

FIG. 2A illustrates a computing architecture 2000 for the IDXDP andrelated processes implemented in the illustrative environment 1000 ofFIG. 1A. In embodiments, the IDXDP and related processes are utilizedwithin an Interactive Development Environment (IDE) as a user develops adigital application. That is, the IDXDP lives within a context stream ofIDE interactions, i.e., actions by the user within the IDE to developthe digital application. In this way, the intelligent digitalapplication development platform can determine what interactions areoccurring between the user and the IDE in order to assist a user incompleting the developer's objectives in an efficient manner, i.e., byimplementing the different modules, components, sub-systems and relatedfunctionality described herein.

For example, in embodiments, the IDXDP and related processes are able todetermine what is being built or structured by the user, e.g., objectcomponents, field values, and other metadata, and use this informationto provide recommendations or other assistance. After determining theuser actions, the IDXDP and related processes is able to derive anintent of the user from the user actions. By considering the intent ofthe user, the IDXDP and related processes are able to providerecommendations and/or actions to the user that matches the intent ofthe user, amongst other functionality as described herein. Theserecommendations can be, e.g., best practices, building tutorials, eventprocessing, implementing the functionality of an autonomous advisor,building and/or recommending different templates, etc.

Various techniques, e.g., AI, NLP and machine learning, amongst others,are implemented by the IDXDP and related processes to provide thefeatures described herein. This may include, e.g., match theextrapolated user actions with user intents and then provide the relatedrecommendations or other functionality as requested by the user oranticipated by the systems and methods described herein. For example, bygathering the actions and intents of the user and matching themtogether, the IDXDP and related processes are able to make keyrecommendations on the likelihood that a similar object or componentalready exists and, using the existing object, etc, make suggestions tothe user. This allows a user to create a digital application in anaccurate and efficient manner by providing outcomes directly within theIDE during the development of the digital application to the user basedon the user's intent.

In further embodiments, the intelligent digital application developmentplatform can represent objects, components or actions by implementing agranular event process. In embodiments, this granular event processrepresents user actions, object component settings, field values andother metadata, which tell a story of what a user is implementing andtrying to achieve within the IDE. More specifically, the granularityprovided by the granular event process comprises an event-orientedrepresentation, i.e., the representation of user actions performedwithin the IDE, and the accompanying syntax associated with the useractions. In this way, a full fidelity and accurate depiction of awidget, service or other software component is captured directly fromthe actions of the user.

Also, the IDXDP serves as a content feed for AI, machine learning andanalytics based engines to determine how developers and other users areconstructing components, and ultimately digital applications. Morespecifically, the AI and machine learning algorithms of these variousengines are able to determine insight into user actions, such asprogramming techniques, to determine improvements in design, developmentpractices and techniques, amongst other areas. The IDXDP also providesan evolution of a particular object from the collection of events andany physical user interactions, e.g., dragging and dropping carried outby a user, that are applied to the object. This results in a logicalobject of its own, e.g., like an authentication user name field, whichevolves from the collected information.

In embodiments, the computing architecture 2000 can determine an intentof a user by capturing user actions, e.g., colorization of an object,and/or other events, e.g., physical interactions with the IDE, at arelatively high level during the development of a digital application.The capture of these events allows for key recommendations or otherfunctionality described herein, e.g., advising of certain actions,tutorials, upselling, determining development issues (e.g., dead ending,etc.), standardizing code, etc. to be provided to and received by theuser in response to these events within the IDE itself, allowing theuser to see the recommendations and implement them immediately.

Referring still to FIG. 2A, the computing architecture 2000 includes abot user interface 2010, which correlates with the interactive advisor1205 of FIG. 1C. The bot user interface 2010 can be a visual interfacethat lives within the context of the IDE and provides recommendations,answers, actions, or other information desirable to the user. Forexample, if a user is utilizing a particular IDE on a laptop, and theIDE is being viewed as a full-screen on the laptop, the bot userinterface 2010 is configured to be movable outside of the IDE.Alternatively, the bot user interface 2010 can be independent of theIDE. For example, if the IDE is being utilized as a full screen on onemonitor and the user is also using an auxiliary monitor, the bot userinterface 2010 can be presented on the auxiliary monitor outside of thecontext of the IDE. In this way, the bot user interface 2010 isconfigurable to the computing real estate available to the user.

The bot user interface 2010 interacts with a bot AI/NLP 2020. Inembodiments, the bot AI/NLP 2020 comprises AI and NLP engines configuredto determine insight into programming techniques of users for theimprovement of design and development practices and techniques. The AIand NLP can be used in any of the modules or components described hereinin order to provide the related functionality as described in therespective sections of this disclosure. In embodiments, the AI and NLPprocessing engines of the bot AI/NLP 2020 are seeded by specificutterances and intents of the user. For example, a user can utter “showme authentication forms in marketplace,” which results in a pop-upwindow displaying a web browser, with the web browser directed to amarket place, e.g., Kony Marketplace™ by Kony, Inc. In furtherembodiments, the web browser shows a specific filter of authenticationroutines within the marketplace. The results will be fetched viaintegration API's and shown in a visualizer tool (e.g., Kony Visualizer®by Kony Inc.) or other development tool. In embodiments, direction to amarket place should not occur until the point of download.

A development tool (e.g., Kony Visualizer® by Kony, Inc.) 2030 withinthe IDE provides a means and apparatus to create assets such as forms,labels on forms, browser widgets and other widgets within forms, amongstother examples, for the development of digital applications. Inembodiments, the development tool 2030 can be any development toolwithin or outside of the IDE, as a non-limiting example. Accordingly,hereinafter the development tool 2030 should be understood to be used interms of any generic development tool, with a preference for the KonyVisualizer® tool being implemented in the environments described herein.

The development tool 2030 can be used by a citizen developer (e.g., asalesperson), a developer with a highly specialized background (e.g., aprogram manager of an enterprise), a designer or other user.Accordingly, the IDXDP and related processes can accommodate andaccumulate information on a variety of backgrounds and abilities. As theuser is performing different actions, i.e., events, in the developmenttool 2030, these actions are tracked and evidence of these actions iscollected by the development tool 2030 and its related components, inaddition to, in embodiments, the AI for analysis and implementing of thedesired functionalities of the different modules (e.g., modules of FIG.1C or other components or systems described herein). Specifically, asthe user performs actions within the IDE, assets, e.g., design elementssuch as a login form, are playing back onto the IDE and renderingthemselves out. This is referred to as a granular event process.

In embodiments, any action taken by the user within the IDE can resultin creation of a fully described widget action (or other asset) havingformat syntax and context. It is this syntax and context that iscollected and analyzed by the IDXDP and related processes. As anon-limiting example, as a user drags and drops a widget (or otherasset) into a form within the development tool 2030, the state of thisaction is collected so that a determination can be made of how aspecific object is being built. In this way, the IDE is extended bydevelopment tool 2030 in such a way that the IDXDP and related processesis able to tap into the event stream within the IDE, learn from thisevent stream and provide recommendations based on this event stream.Therefore, the IDXDP utilizes assets as a means for learning about theassets.

In particular embodiments, the IDXDP collects and processes user actionsas events, which further represent the construction of a logical object.In embodiments, the construction of objects comprises various actionsand/or various levels. As a non-limiting example, the object can becreated by a user dragging and dropping a particular widget into an IDEthrough the development tool 2030, or a user clicking onto a text box toadd text within the object. In embodiments, the user may further refinethat particular text object or text box in many ways. For example, theuser may change a foreground color or a background color or a font usedfor any text within that box or change the style of the text, e.g.,boldness, italicization etc. By collecting and processing these andother user actions, over time, the IDXDP can determine what a user'sintent is and how the user is building that particular object. Thisinformation can also be used as seed information of the AI, provided toand by the different bots, AI or other modules or tools to provide thefunctionality described herein.

Depending on the textual content of the object, some degree ofassertions can be made about what that particular object will do andwhat it will be used for. As a non-limiting example, if the object is atext box, and the text box is pre-initialized with a term, e.g., ausername, then the IDXDP knows that the form is affiliated withauthentication. In this way, the IDXDP considers all actions done by theuser. Acquiring the context via an event system and general scan of thevisualizer state is an objective of the IQ event system. The contextcould come not necessarily from the widgets but also from what is nameda particular form or module as within the visualizer. Therefore,assumptions can be made and further recommendations can be made based onthese assumptions. A further example would be that of anticipating theresizing of an object such that it matches the symmetry of pre-existingobjects. In this example, when a user begins to resize an object, theobject then snaps to a virtual grid line. This grid line would be eitherhorizontally or vertically oriented as determined by the AI subsystem.

Still referring to FIG. 2A, the development tool 2030 can includemultiple sub-systems, each of which cooperate in a coordinated fashionto perform specific tasks in collecting and processing the user'sactions. The sub-systems include the enhanced event engine 2040, theevent queue processor 2050 and the queue manager 2060. These varioussub-systems can also be stand-alone systems or integrated systems orcombinations thereof within the IDXDP, any of which combination willcommunicate with one another through events. Specifically, onesub-system raises an event to which the other sub-systems subscribe,i.e., triggered when the event is fired, and react according to theneeds of the development tool 2030. In embodiments, the development tool2030 is a subscriber to all the events raised by various sub-systems,modules, processes and tools described herein.

In embodiments, the enhanced event engine 2040 collects evidence of theuser's actions, while the event queue processor 2050 receives thecollected evidence from the enhanced event engine 2040. In embodiments,as the user performs various actions in the development tool 2030, e.g.,adding a text box, the enhanced event engine 2040 collects evidence ofthese actions and places this information within a queue of the eventqueue processor 2050. Specifically, all events, i.e., user actions inthe development tool 2030, are queued asynchronously into the queue ofthe event queue processor 2050. In embodiments, the enhanced eventengine 2040 includes logic to perform the actions described herein. Forexample, the enhanced event engine 2040 may include natural logic, e.g.,NLP, to determine that a user has entered text within the developmenttool 2030 and to collect this action.

The event queue manager, i.e., queue manager 2060, pops out each eventfrom the queue of the event queue processor 2050, and processes thoseevents into structured objects. As should be understood by those ofskill in the art, an event can be any creation of an asset, e.g.,object, template, widget, etc. In embodiments, the queue manager 2060collects the information, i.e., evidence of user actions, in a stylesuch as JavaScript object notation (JSON) format. These structuredobjects can then be used to comparison, matching and recommendations, inaddition to the actually seed for learning by the AI.

The queue manager 2060 will take the collected evidence out of the queueand provide it to the event analytics engine 2070 for furtherprocessing. In this way, each user action performed within the bot userinterface 2010 and/or the development tool 2030 essentially results inthe enhanced event engine 2040 interpreting the user action, storingcollected information regarding the user action in a queue of the eventqueue processor 2050, removing and processing the collected informationby the queue manager 2060, evaluating the processed information throughthe event analytics engine 2070 and the event AI/NLP engine 2080, andthen providing a recommendation or other functionality provided herein,which can be implemented in the modules of FIG. 1C or the othercomponents described below.

The information collected from the evidence includes, e.g.:

1) user actions to create, rename, delete forms, skins, widgets or otherassets, etc.;

2) corresponding actions/events raised by various sub-systems describedherein;

3) captured information like the name of the form, skin, specific useractivity, channel that the user is building the application for;

4) additional contextual information about the application, e.g., appname and domain, for example; and

5) JavaScript source code changes.

With this information, a widget, an object, or other asset, e.g., aslider, a control, a label, a flex form or a browser widget, amongstother examples, has a complete representation that is not ambiguous.More particularly, the IDXDP provides a clear representation in anon-ambiguous form in a specific style, e.g., JSON. In this way, asoftware syntactical definition of a logical object such as a componentor widget, is provided, which can then be used to provide the differentfeatures described herein such as providing recommendations of bestpractices, tutorials, standardizing code, etc., or other functionalitydescribed herein. In embodiments, a widget, object services or otherasset can be described as name values or value pairs, for example, inJSON.

Further, the information collected can have metadata associatedtherewith, which can be used for additional purposes such as dateinformation, identification information, etc. In embodiments, themetadata can be editable, can be logical or can be immutable, amongstother examples. In embodiments, the ancillary metadata can be used as anexecutable component to recreate a particular object by analyzing themetadata. For example, by knowing the user intent as determined by theAI and/or bots described herein, the IDXDP can understand the goal ofthe user and use this information to implement the functionality of thedifferent modules, components and subsystems described herein. Forexample, by analyzing the metadata, an object can be constructed and/orreconstructed in line with the user's intent, thereby allowing the IDEto act as a visual renderer, a teacher at the same time, by playing backthe construction/reconstruction of the object and, if needed, providingan appropriate recommendation.

The metadata can also be used to create a profile of the component orcollection of services and components, etc. that makes the object easierto interpret through AI techniques and other machine learningtechniques. For example, the metadata surrounding the object may beembedded with a message conveying information about the object.Accordingly, the systems and processes described herein allow for amethod for interpreting the component data and associated metadata toallow processing within an AI and machine learning layer.

As another example, the metadata around a particular object may beencoded with a message for another user, with the message being conveyedto the other user as a real-time rendering of the object occurs withinthe IDE. As another example, the metadata may be provided by anindividual(s) that created the object. In the scenario that multipleindividuals contributed to creating the object, the IDXDP provides theability to rate those component editorial metadata collections in thecase where one might be clearly a five-star, while another one might bea four star and two of them might be three stars, etc. In furtherembodiments, the various ratings can be filtered, depending on theuser's needs. Accordingly, the processes described herein can provide aformat and an extensibility syntax, which will allow the functionalcomponent or object to be extended by related or supportive metadata.

In embodiments, the processed information is stored in a database, e.g.,non structured query language (NoSQL) database, and provided to theevent analytics engine 2070 for analysis and further use in accordancewith the many aspects of the present disclosure. In embodiments, thedatabase can be the storage system 1022B shown in FIG. 1A.

The event analytics engine 2070 is responsible for analyzing the aboveinformation to determine whether there are other steps to perform, ifany, and the outcomes, which match the actions of the user. Inembodiments, the event analytics engine 2070 is organic to the types ofevents that are being generated through the IDE. Particularly, the eventanalytics engine 2070 makes sense of the events that are flowing throughthe IDE by analyzing the processed information. For example, the eventanalytics engine 2070 can analyze the information to determine whetherthe user is not providing standard code, not practicing best practices,dead-ending, etc., in order to thus provide certain recommendations.

In conjunction with the event analytics engine 2070, the event AI/NLPengine 2080 determines what types of actions were undertaken by the userin order to determine an appropriate recommendation, e.g., action to betaken by the user or an answer that a user desires, amongst otherexamples. These recommendations can be coding recommendations, designrecommendations, etc., as described herein. In embodiments, the eventAI/NLP engine 2080 comprises AI, machine learning and NLP enginesconfigured to determine the intent of the user. The event analyticsengine 2070 and the event AI/NLP engine 2080 can be referred to as thebrains of the computing architecture 2000, in addition to the overallbrain of the IDXDP. If desired, further “brains” can also be chained tothe event analytics engine 2070 and/or the event AI/NLP engine 2080. Forexample, an additional brain (AI) can be chained in which the additionalbrain only serves to notify when a particular build is available. Asanother example, a security module (AI) can be chained that informs theuser of particular security paradigms as they build their application,or coupling in paid licenses for other system tool kits (STKs) orsoftware development kits (SDKs) that keep the end application secure.

In embodiments, the AI/NLP engine 2080 can be used as a content feed ofevents for AI, machine learning and analytics based engines fordetermining how developers and other users are constructing components,and ultimately digital applications. Specifically, the AI and machinelearning algorithms are able to determine insight into programmingtechniques to determine how to improve design and development practicesand techniques. This can then be used to learn best practices or mostefficient means of performing a certain action, and to subsequentlyprovide recommendations to the user based on their intent.

By way of a more specific example, the event analytics engine 2070 andevent AI/NLP engine 2080 can determine whether a certain action taken bythe user is a preferred action (e.g., most efficient way of performingan action) and, if not, provide recommendations of different actions tobe taken which may be more preferred, e.g., placement or look of awidget, etc., as taken by another user tasks with a similar issue. As anon-limiting example, if a slider widget is selected by the user, theevent analytics engine 2070 and the event AI/NLP engine 2080 know thatthis widget is a slider because of the syntax of the object. As anotherexample, if a user clicks on a slider widget, the event analytics engine2070 together with the event AI/NLP engine 2080 can determine that aslider widget was clicked. These different user actions can then beanalyzed by the event analytics engine 2070 and event AI/NLP engine 2080to determine whether such actions match with approved and/or standardactions, and whether any recommendations are required to provide insight(tutorials) and/or assistance (recommendations) to the user.

In embodiments, the AI engine of the event AI/NLP engine 2080 can matchuser actions with intents, while the NLP engine matches phrases andutterances with the intents of the user. For example, the AI enginerecognizes that the user is implementing a textbox pre-initialized witha “username,” while the NLP engine recognizes the utterances of the userasking for authentication routines. The event AI/NLP engine 2080 willmake a determination that the user is attempting to create a securityrelated objected based on the recognitions of the AI engine and the NLPengine. In this way, the event AI/NLP engine 2080 provides astep-by-step analysis of what a user, such as a developer, is doingwithin the IDE by considering and understanding all of the actionsperformed by the user.

In still further embodiments, the recommendation may be from othersources (e.g., users) obtained from or learned by the event AI/NLPengine 2080. More specifically, the event analytics engine 2070 andevent AI/NLP engine 2080 are configured to receive and analyze “bigdata,” i.e., data from any and/or all users of the IDE, including userswithin and outside of an enterprise. In this way, the IDXDP can providerecommendations not only based on the actions of a single user, but alsorecommendations based on the actions of many users or a community ofusers. For example, the recommendations may have been performed by othercommunity members and may have been determined to be best practices inthe industry. Accordingly, the event analytics engine 2070 and the eventAI/NLP engine 2080 can obtain and analyze a global or local community ofdata.

In further embodiments, the computing architecture 2000 can include adispatcher, i.e., a bot director, which can manage the journey of theanalytics all the way from the bot user interface 2010, through achainable infrastructure, or a chainable architecture comprising aparticular chain of brains, resulting in a certain output, i.e.,recommendation. In embodiments, any of the brains of the IDXDP andrelated processes can be relatively simple, can be relatively complex,and can be reordered in their processing order so that certain brainsmay tend to return a value closer to a top of the stack of values that auser may be interpreting or receiving in the bot user interface 2010.

As shown in FIG. 2A, a recommendation engine 2090 filters all the waythrough to the queue architecture, i.e., the event queue processor 2050and the queue manager 2060, and all the way back to the bot userinterface 2010, to provide a recommendation, answer or other informationto the user. The recommendation aides the user in their digitalapplication development by providing the functionality described herein.As a non-limiting example, if the event analytics engine 2070 and/or theevent AI/NLP engine 2080 find redundancies in the digital applicationdevelopment, the recommendation engine 2090 can provide a recommendationfor a pre-existing instance of an object and/or outcome which matchesthe intent of the user, and directly to a point where the object shouldbe inserted. In this way, the user is able to directly implement therecommendation within the IDE.

As another example, if a user is at a third step of a process, therecommendation engine 2090 can provide recommendations which match theremaining steps the user intended to pursue and/or the outcome the userintended and, in embodiments, inject the remaining steps directed intothe development tool. For example, the recommendation engine 2090 canmake recommendations, such as the remaining steps in a process ofconstructing an object, which were compared and matched from other formsbased on a historical perspective of previous user actions or as foundwithin a community of users (e.g., as searched through blogs or otherdevelopment tools, databases storing digital application information,etc., for example). Specifically, the steps and/outcome may already bein the user's workspace or a shared team workspace within an enterprise,a marketplace full of items, or a couple of instances across theInternet that exist, amongst other examples. In addition, therecommendation engine 2090 may provide the user with recommendationsthey were not aware of.

The event analytics engine 2070 and/or the event AI/NLP engine 2080 alsoprovide information concerning the user to the recommendation engine2090, for determining the appropriate recommendation. As a non-limitingexample, the recommendation can be highly technical if the user is adeveloper. For example, if the event analytics engine 2070 and/or theevent AI/NLP engine 2080 determines that the objects in the developmenttool 2030 are in a horizontal and a vertical orientation, adetermination is made that the user is trying to create a calculator.Since the user is a developer, the recommendation engine 2090 canrecommend a technical object like a padding manager, which providesequal spacing and padding management to the widgets already laid out inthe development tool 2030. The padding manager recommendation would beunderstood by the user, since the user has an extensive background inapplication development. Other recommendations include refactoring code,or defect tracking, for example. In this way, the recommendationsprovided by the recommendation engine 2090 can be wide, broad and deep,or can be specific and narrow, specifically tailored to the user and theuser's needs.

A recommendation notification adapter 2100 is configured to gauge how aparticular recommendation should be serviced, i.e., provided to the userso that the user is given the best opportunity to understand therecommendation (whether to provide a script or a speech responsedetailing the instructions within the script). Specifically, therecommendation notification adapter 2100 can coordinate with the eventAI/NLP engine 2080 to determine in what order a particularrecommendation is displayed or how the recommendation is weighted.

In embodiments, a confidence level for the recommendation is calculatedand used to determine if the recommendation should be presented to auser. The confidence level can be calculated by considering variousparameters along with weighting the parameters. Examples of parametersinclude hierarchy of flexes, orientation of widgets, method names, skinswith images, and data of the widgets, amongst other examples. Further,different widgets can have different thresholds, which are also takeninto consideration. As a non-limiting example, basic widgets withincontainer widgets need siblings to attain a threshold. All of theseparameters can be considered in determining the confidence level.

For an identified pattern to be shown as a recommendation by therecommendation engine 2090, the recommendation can stream through anumber of various filters, namely hierarchical, visual, textual anddomain related. The confidence of a particular recommendation can becalculated by the following formula shown in equation (1) using therecommendation notification adapter 2100:

Total Confidence=1−(a*H _(D) +b*V _(D) +c*T _(D))  (1)

where H_(D)=hierarchical difference between target and source,V_(D)=visual difference between target and source, T_(D)=textualdifference between target and source, and a, b, c are the weightingsgiven for each difference. In embodiments, a measurement of totalconfidence is the measurement that is attained once all the differencesare eliminated from a hypothetical exact match. For example, when thehierarchical differences, the visual differences and the textualdifferences are all equal to zero, the total confidence will be 1, i.e.,100%. When the total confidence level of a specific pattern overcomes apredetermined confidence threshold, that pattern can be considered as arecommendation and ranked on its confidence level.

The difference in each filter is calculated only when certain criteriaare met with precedent calculations, i.e., in calculating the visualdifference V_(D) there should a certain hierarchical difference H_(D).Specifically, the hierarchical difference H_(D) should be less than aprovided threshold H_(T), because having a relatively large hierarchicaldifference can render calculating the visual difference moot. Therefore,the difference considerations should be performed as shown in equation(2).

Hierarchical(H _(D) <H _(T))→Visual(V _(D) <V _(T))→Textual  (2)

where H_(T)=hierarchical threshold and V_(T)=visual threshold.

Calculating the hierarchical difference is shown in equation (3).

$\begin{matrix}{H_{D} = \frac{{len}\left( {Matched}_{pattern} \right)}{\max\left( \left( {{{len}\left( {Source}_{pattern} \right)},{{len}\left( {Target}_{pattern} \right)}} \right. \right.}} & (3)\end{matrix}$

Specifically, equation (3) provides the percent of a pattern that ismatched, and will be carried forwards to calculate the visual differenceif H_(D)<H_(T).

Calculating the visual difference is shown in equation (4).

$\begin{matrix}{V_{D} = \frac{\sum\limits_{i = 0}^{N}X_{i}}{2*\left( {N - 1} \right)}} & (4)\end{matrix}$

where X_(i)=visual difference of each widget and N=number of widgets inthe identified pattern. When calculating the visual difference for eachwidget, if two widgets are of a same type, then a difference is takenbetween their properties. If a property is the same for both widgets,then there is no visual difference between the widgets. However, ifthere is a difference in their properties, then the predeterminedweighting is added to the visual difference of that widget and continuedfor the rest of the properties. In this way, a visual difference iscalculated for each widget present in the identified pattern and thevisual difference V_(D) for the complete pattern is calculated.

In calculating the textual difference T_(D), each widget tree can berepresented as a list of all the words present in text, placeholder,etc., properties. In embodiments, this list can be referred to as asentence. In calculating the similarity between two words, it is assumedthat each word is represented by a vector of some fixed length. Thisfixed length may result in a problem of finding a metric of differencebetween two clusters of points in an n-dimensional space, where eachcluster is a sentence and each point is the vector representation of theword in the sentence. Therefore, multiple approaches can be undertaken.In the first approach, the closest point difference is calculated. Forthis approach, each point in the first cluster is iterated over, whilethe closest point in a second cluster is found. In embodiments, aEuclidean distance between these two points is taken to find the textualdifference T_(D). The point from the second cluster is removed and theprocess is repeated if need be. Under this first approach, some edgecases may need to be given more thought, like when the clusters have adifferent number of points and some of the points are outside a boundaryof the cluster.

For the second approach in calculating T_(D), a mean point of eachcluster is calculated, and the difference between the mean points ofeach cluster equals the textual difference T_(D). The difference betweenthe mean points of each cluster can be a Euclidean distance or a cosinesimilarity, amongst other examples. In the case of marketplacerecommendations, another parameter called domain is considered. Inembodiments, domain filtering is taken prior to hierarchical or visualfiltering.

Referring to equation (1), after calculating the respective differences,i.e., hierarchical, visual and textual, all of the differences areaccumulated by multiplying each difference with their respectiveweightings. This value is then subtracted from “1” to get a totalconfidence of the identified pattern. In the situation where a patterndoes not meet a confidence threshold, that pattern is not provided as arecommendation and is taken out from the recommendations queue of therecommendation engine 2090. In this scenario, the recommendation engine2090 can generate a new recommendation, which will then go through theconfidence level process just described. The new recommendation can bebased on the actions of the user, which were used for the originalrecommendation, or other actions, such as subsequent user actions afterthe recommendation did not pass the threshold. The recommendation canalso be an extrapolation of the user's current action, or a request thatthe user take a further action, different than the current action beingtaken.

The recommendation bot extension 2110 and the recommendation actionextension 2120 are configured to operate with the bot user interface2010, providing the user with a tailored experience. In embodiments, therecommendation bot extension 2110 is configured to provide the user withrecommendations in verbal, textual or audio formats. The recommendationsof the recommendation bot extension 2110 are offered to the user througha digital user interface, either through the bot user interface 2010 orspoken word using a speech technology, for example.

In embodiments, the recommendation action extension 2110 is configuredto complete an action on-screen. More specifically, the recommendationaction extension 2110 not only provides textual output back to the botuser interface 2010, but is also configured to be able to eitherpopulate a form of a widget or some other real tangible action. In thisway, rather than simply conveying something back in written form to theuser, the recommendation action extension 2110 takes a recommendationfrom the recommendation engine 2090 and can actually fulfill it. In thisway, the recommendation action extension 2110 can provide a particularrecommended action on-screen and within the project a user is currentlydeveloping, or can fulfill the action while the user is developing theirdigital application.

In embodiments, for a natural language component, such as if a user asksa question through the bot user interface 2010, the question goes to theAI/NLP layer and comes back through the recommendation engine 2090 bybeing routed through either the recommendation bot extension 2110 or therecommendation action extension 2120. For example, an utterance by theuser of “can you show me authentication forms in marketplace?” wouldreturn a result to a marketplace with a specific search filter forauthentication routines. As a further example, a user could type intothe bot user interface 2010 “show me some of the forms that exist in myworkspace.” The bot AI/NLP 2020 then takes a look and searches through auser's own workspace to find a basic similarity between what the user iscurrently doing and what it has done in the past. Alternatively, the botAI/NLP 2020 can search marketplaces on the Internet, intranet or otherextranet (e.g., the cloud computing nodes 1110 shown in FIG. 1B),looking at forms and widgets and fully formed applications that aresimilar to what a user is producing and suggest incorporation ofprewritten forms into the user's own digital application.

In embodiments, the user can opt out of the IDXDP at any point. Byopting out, the IDXDP and related processes will not track and collectinformation concerning the user's actions within the development tool2030. Further, the user will not receive any recommendations. If a userdoes opt out, the IDXDP can encourage a user to opt in by providingdetailed information on how others are benefiting from the IDXDP. Forexample, the IDXDP provides a story to the user of how other user withinthe enterprise are benefiting from the features of the IDXDP.

FIG. 2B illustrates a swim diagram 2300 with the following actors: thedevelopment tool (including modules M1 and M2) 2030, enhanced eventengine development tool 2040, event queue processor 2050, and queuemanager 2060 from FIG. 2A. As the user performs actions in thedevelopment tool 2030, the actions are collected and the informationprovided by the actions are then utilized to determine an intent andprovide a recommendation to the user based on the intent. For example,at step 2305, the first module M1 of the development tool 2030 registersan event X, i.e., user action, with an event name. At step 2310, thesecond module M2 of the development tool 2030 subscribes to the event X,i.e., is triggered when the event X is fired, that is raised by thefirst module M1, i.e., fired. At step 2315, the first module M1 willthen raise the event X to a visualizer event queue manager, i.e., thequeue manager 2060. At step 2320, the visualizer event queue managerrequests additional context for the first module M1 and the event X. Atstep 2325, the development tool 2030 will respond with the additionalinformation about the event X that is requested from the first moduleM1. At step 2335, the development tool 2030 will process the eventinformation and create a structured object. At step 2340, the secondmodule M2 will trigger the event X with the structured object.

Event Processing System and Method

The event processing system and method is a system and method capable ofutilization within an IDE as a user develops a digital application. Inembodiments, the event processing allows for datamining in order todetermine if a similar object or component or other asset as definedherein already exists, which can then be used to providerecommendations, etc. Specifically, the systems and processes describedherein allow for processing an event queue of the event queue processor2050 shown in FIG. 2A.

In embodiments, the event queue is comprised of IDE user actions withthe ability to construct an object or service from that data within thatevent queue. In this way, the event queue can drive recommendations todevelopers, designers, and all other users using the IDXDP as describedherein. In embodiments, the recommendations can take the form of textualcontent, spoken content or even actions, e.g., running an authenticationscript.

In further embodiments, natural language narrations are dynamicallyformed and rendered to the user to create a depth of understandingrelated to the composition and capabilities of a component or object orother asset type described herein. In still further embodiments, themachine learning component continuously optimizes designs of assets andoffers the latest embodiment to the user. In this way, the IDXDP canprovide current recommendations applicable for the user now with respectto developing a digital application.

The systems and processes described herein continuously monitor thecomplexity of a code used in the construction of an object or otherasset type to determine if the code is becoming too complex, i.e.,having code snippets which are unnecessary to create a particularoutcome. In embodiments, if a determination is made that the code is toocomplex, the AI of the IDXDP can suggest how to optimize the code inorder to reduce complexity, i.e., providing a code snippet that allowsthe user to achieve the desired outcome.

The systems and processes also continuously monitor the code for bestpractices or coding standards. In embodiments, for example, if the userdoes not follow a best practice, the system is configured to use a chatinterface or an audio interface, amongst other examples, to inform theuser that they are not following the best practice. The best practicecan be determined from industry practices or an enterprise team ofdevelopers, amongst other examples. In further embodiments, the system,using code optimization intelligence built therein, i.e., machinelearning logic, can provide suggestions to the user for correcting thecode in order to maintain best practices. In further embodiments, thesystems and processes can develop a common coding style based onmonitoring the practices of team members or other community of users.The systems and processes can use this and other information to suggestways for the team to standardize their coding in order to make the codemore maintainable. For example, the system can establish a best practicebased on feedback from the team or other user community.

Additionally, the systems and methods can store common coding stylesacross all developers (or other users) to suggest best practices on anenterprise level, as well as within a sub-group within the enterprise.In addition, the systems and processes described herein can suggest acomponent in a marketplace that may be a good fit, i.e., allows the userto achieve their desired outcome, for a component the user isdeveloping. In embodiments, the suggestion can be based on analyzing theobject or visual component names being used by the developer or otheruser, and by inspecting the behaviors of the user who created thecomponent, amongst other examples.

FIG. 3A illustrates several screen shots 3000 showing the capabilitiesprovided by the event queue of the event queue processor 2050 inaccordance with aspects of the present disclosure. During thedevelopment of a digital application within an IDE, for example, a usercan utilize a direct bot interaction interface 3010 capability of thebot user interface 2010 of the interactive advisor module 1205 as shownin FIG. 2A. In embodiments, the user can type questions, comments orrequests into the user interface and expect a reply, e.g., a pop-upwindow taking a user to a marketplace, as shown in FIG. 3A. As anon-limiting example, the user may type into the direct bot interactioninterface 3010 “make this form a master.” The interactive advisor module1205 of FIG. 1C is configured to respond to this request, through thedirect bot interaction interface 3010, by responding that the request isdone. For example, the direct bot interaction interface 3010 may respondwith a uniform resource locator (URL) where the master is located, e.g.,a marketplace. This form can also imported into the user's library ordistributed within the enterprise, amongst other examples.

Alternatively, a user may forego directly typing into the direct botinteraction interface 3010 and may utilize the autonomous guidanceinterface 3020 capability of the bot user interface 2010. The autonomousguidance interface 3020 capability of the bot user interface 2010 may beimplemented with the autonomous advisor module 1210 shown in FIG. 1C.

In using the autonomous guidance interface 3020 capability of the botuser interface 2010, a user may be constructing an object in the IDEthrough the development tool 2030, while the autonomous advisor module1210 functions in the background. The autonomous guidance interface 3020has the capability of providing recommendations to the user based on theactions undertaken by the user during the development of the digitalapplication. As a non-limiting example, the autonomous guidanceinterface 3020 may provide a pop-up communication box in the IDE,informing the user that what they are constructing a component similarto another component previously constructed.

The autonomous guidance interface 3020 can query the user, e.g., ask ifthe user desires to explore this previously constructed component, andprovides the ability for the user to answer as “yes” or “no” as shown inFIG. 3A. In this way, the autonomous guidance interface 3020 can save auser a substantial amount of time, by providing an object orrecommendation desired by the user, in addition to providing theseoptions without an explicit request that needs to be generated by theuser through the bot user interface 2010. Further, the autonomousguidance interface 3020 allows a user to explore the suggested componentwithout having the component automatically implemented within the IDE.

Still referring to FIG. 3A, the social and technical eminence interface3030 can invoke the technical eminence module 1225 of FIG. 1C. Morespecifically, the social and technical eminence interface 3030, forexample, allows an enterprise, a team, or other user, to share assets.For example, the technical eminence interface 3030 allows users tointeract in a marketplace, e.g., the Kony Marketplace™, amongst otherexamples. Alternatively, users may interact with one another through acommunity, e.g., a developer community, or user groups. In addition,users may share assets at specific locations, such as a web-basedhosting service, e.g., GitHub, or websites which offer developer tips,e.g., Stack Overflow.

The extended analytics interface 3040 helps eliminate issues and createopportunities for an enterprise during the digital applicationdevelopment. For example, the extended analytics interface 3040 providessupport optimization by helping to eliminate trouble tickets using theproduct evolution module 1245 of FIG. 1C. Specifically, a user can beprovided with an answer to a problem they have encountered, or mayencounter based on their actions. For example, a user may encounter aproblem concerning the colorization of a widget. The product evolutionmodule 1245 of FIG. 1C would recognize the problem and provide asolution for coloring the widget through the extended analyticsinterface 3040. In embodiments, the solution may be obtained from adatabase within the enterprise, or a community of users, amongst otherexamples. In this way, the user is provided with a solution before theneed to create a trouble ticket and can continue developing theirdigital application.

The extended analytics interface 3040 also provides a benefit of thirdparty integration capabilities, e.g., salesforce integration, bylistening/searching entire enterprises and determining the breadth withwhich an enterprise is utilizing the IDXDP. For example, the extendedanalytics interface 3040 may recognize that certain groups within theenterprise are utilizing the IDXDP, while another group may have yet toimplement the IDXDP. This information can be used to make a specificrecommendation up to Kony® of Kony, Inc. or other service provider forother opportunities, e.g., an upsell opportunity as described withrespect to FIG. 11A.

The extended analytics interface 3040 also assists in the evolution ofthe IDXDP, and any of its associated components. Specifically, the IDXDPmay require a modification at some point in order to meet the changingneeds of programmers, designers, and other users. For example, theextended analytics interface 3040 has the capability to run a userjourney and send the results of the journey to an engineering team forevaluation and correction of any issues or update of components based onthe user actions. In this way, the IDXDP is not a static platform, butan ever-changing platform to meet the needs of current users. TheAdditional Plugins aspect of the extended analytics interface 3040allows for various plugins, i.e., brains, to be implemented into theIDXDP for further enhancements.

The omni-channel application extensions interface 3050 provides thecapability of AI to be applied directly to industry specific verticalapplications. As a non-limiting example, the AI elements can be appliedto retail banking. Further aspects of the omni-channel applicationextensions 3050 include Field Service, Generic Widgets and MarketplaceHosted, i.e., marketplace hosting.

FIG. 3B shows an illustrative architecture of the event processing andpattern recognition module 3200. In embodiments, the event processingand pattern recognition module 3200 implements equations (1)-(4) inorder to determine specific recommendations and/or actions. For example,the event processing and pattern recognition module 3200 can determinewhere specific widgets or other assets are placed within forms. Asanother example, the event processing and pattern recognition module3200 can make use of the recommendation module 3250 to realize, e.g.,existing assets are similar to what the user is currently developingwithin the IDE by analyzing the user's own workspace.

More specifically, as shown in FIG. 3B, the event processing and patternrecognition module 3200 comprises a development tool 2030, e.g., KonyVisualizer® by Kony, Inc, and recommendation module 3250. Inembodiments, the development tool 2030 is comprised of an API 3230 andevent system 3240. The recommendation module 3250 comprises arecommendation component 3270, a command processor 3280 and anNLP/machine learning component 3290.

In operation, an event stream 3260 comprising user actions performedwithin the development tool 2030 is sent from the event system 3240 tothe recommendation component 3270 of the recommendation module 3250. Therecommendation component 3270 takes the information within the eventstream 3260 and determines whether a recommendation should be providedto a user. Specifically, the recommendation component 3270 communicateswith a command processor 3280 and the NLP/machine learning component3290 in order to determine and provide the recommendation.

In embodiments, the recommendation component 3270 determines arecommendation by utilizing the pattern recognition and code analysisprovided in equations (1)-(4), in conjunction with the NLP and machinelearning features of the NLP/machine learning 3290 component. In orderto accomplish these tasks, the recommendation component 3270 comprisesthe recommendation engine 2090, while the NLP/machine learning component3290 comprises the event AI/NLP engine 2080. For example, the machinelearning feature from the event AI/NLP engine 2080 may take theinformation from the event stream 3260 to learn recognition of anobject. By way of example, the machine learning can learn that theobject is a widget and make recommendations related to widgets. In thisway, the best recommendation is determined based on what has alreadybeen learned. In further embodiments, recommendations based on learnedfeatures are given a heavier weight, i.e., a further weight added to thecalculations for Total confidence in equation (1).

The command processor 3280 takes system information and determineswhether the recommendation should be made. For example, the commandprocessor 3280 is configured to discriminate between whether an actionneeds to take place or whether a textual content recommendation shouldsurface through a bot, i.e., the recommendation bot extension 2110.Further, the command processor 3280 is configured to execute equations(1)-(4) to calculate the total confidence of the recommendation, andalso to determine if the calculated total confidence surpasses athreshold set for approving recommendations. In this way, the commandprocessor 3280 takes the recommendation to a system level and makes therecommendation, which is then forwarded to the event system 3240 forimplementation.

The event system 3240 comprises the recommendation notification adapter2100, which is configured to gauge how a particular recommendationshould be serviced, e.g., which format the recommendation should bepresented to the user. In embodiments, the event system 3240 comprisesthe recommendation action extension 2110, which provides not onlytextual output back to the bot user interface 2010, but is alsoconfigured to populate a form of a widget or other tangible action. Ineven further embodiments, the event system 3240 comprises therecommendation action extension 2120, which is configured to providespecific actions within the development tool 2030 without a user needingto interact with the bot user interface 2010. In this way, therecommendation can be an actual action or textual content or othersuitable content.

Still referring to FIG. 3B, the API 3230 communicates between thedevelopment tool 2030 and the NLP/machine learning component 3290 toprovide requests 3310 to and receive suggestions 3300 from theNLP/machine learning component 3290. In embodiments, the API 3230,through the interactive advisor module 1205, is configured to answerquestions of the particular user, whether it be the developer, designer,administrator, etc. using the AI. The requests can be generated by theuser during the development of their digital application through theuser interface 2010, as a non-limiting example. The suggestions can beprovided to the user through the API 3230 as textual content or spokencontent, amongst other examples.

The recommendation module 3250 will continuously monitor the complexityof a code used in the construction of an object or other assert type todetermine if the code is becoming too complex, i.e., having codesnippets which are unnecessary to create a particular outcome, throughthe recommendation component 3270. In embodiments, if a determination ismade that the code is too complex, the AI of the IDXDP, and particularlythe AI of the event AI/NLP engine 2080, can suggest how to optimize thecode in order to reduce complexity. Complexity is determined bycomparing the code to a library of machine learning outcomes stored in adatabase, e.g., storage system 1022B of FIG. 1A. For example, a snippetof code from a machine learning outcome can be compared to the codebeing developed by the developer.

When the code becomes too complex, the AI of the recommendation module3250 and/or recommendation component 3270, e.g., the NLP/machinelearning component 3290 through the event AI/NLP engine 2080, providessuggestions for optimizing the code in order to reduce complexity. Forexample, a suggestion from the recommendation module 3250 and/orrecommendation component 3270 may run a script through therecommendation action extension 2120, which essentially thins the codedirectly as an action. Further, the recommendation module 3250 and/orrecommendation component 3270 can suggest to run this scriptautomatically so that the code complexity can be reduced in an efficientmanner. As another example, a suggestion through the recommendation botextension 2110 and shown through the bot user interface 2010 to the usermay be examples of a plurality of routines that provide the same outcomethe user desires, but do so at a much greater degree of efficiency, interms of the code running, or a much higher degree of maintainability.

The recommendation module 3250 and/or recommendation component 3270 alsocontinuously monitors the code for best practices or coding standards.In embodiments, if a user does not follow a best practice, therecommendation module 3250 and/or recommendation component 3270 uses achat interface or audio interface, e.g., through the bot user interface2010 through the API 3230, to inform the user they are not following abest practice. In further embodiments, the recommendation module 3250suggests how to correct the problem to maintain best practices usingcode optimization intelligence built into the recommendation component3270.

The best practices can be found in manuals or learned practices, amongstother examples, stored in databases, e.g., storage system 1022B of FIG.1A. For example, a best practice in JavaScript realm suggests that auser terminate particular executable lines with a semicolon. In thisway, the best practices could be things as simple as initializingvariables from a front end, eliminating the use of global variables,providing the right degree of syntactical correctness, e.g., semicolons,at the end of lines. Alternatively, the best practices may be morecomplex, depending on the technical capability of the user. A chatwindow can be provided through the bot user interface 2010 to inform theuser they are not following a best practice. For example, the chatwindow may suggest how to correct the code to be consistent with bestpractices. Specifically, a bot associated with the chat window, i.e.,the through the bot user interface 2010, can provide a chat boxindicating best practices. In embodiments, a user can click “yes” or“no” in response to a prompt to either accept changes to be made or todeny the changes.

In further embodiments, the recommendation module 3250 and/orrecommendation component 3270 can develop or recommend a common codingstyle based on monitoring the practices of team members (or othercommunities) and suggest ways for the team to standardize their codingto make the code more maintainable. For example, a majority of the teammay use tabs to indent their code, while only a few members use spaces.Since the majority of team members use tabs, the recommendation module3250 and/or recommendation component 3270 would suggest that everyoneuse tabs in order to accomplish a common coding style across all users.

The recommendation module 3250 and/or recommendation component 3270 canalso establish a best practice based on feedback from the team or aglobal community on a proposed best practice. For example, when there isa team of enterprise developers that are coding a certain project, it isdesirable for the code to look the same from all user contributions forfuture users. In this way, the IDXDP has the ability to analyze the codeassociated with an entire enterprise in order to derive the bestpractices or common practices that that enterprise uses.

In further embodiments, the recommendation module 3250 and/orrecommendation component 3270 can store common coding styles across alldevelopers to suggest best practices on an enterprise level, as well asother levels, such as all users within a group of the enterprise. Inembodiments, the recommendations/suggestions from the recommendationmodule 3250 can match the technical level of the specific user, e.g., apadding manager recommendation for a developer. In addition, therecommendation module 3250 and/or recommendation component 3270 cansuggest a component in the marketplace as a substitute for a componentbeing written by a developer, based on analyzing the object or visualcomponent names being used by the developer and inspecting the behaviorsthe user is creating for the user created component. These assets can beassets that live on the World Wide Web, amongst other examples.

FIG. 3C illustrates a chat client, i.e., the API 3230, within thedevelopment tool 2030. In embodiments, the API 3230, through interfacessuch as the bot user interface 2010, allows developers and other usersto communicate with one another and bots of the IDXDP. Specifically, theAPI 3230 allows for direct communication with bots, e.g., Kony® of Kony,Inc. bots, e.g., the bot AI/NLP 2020, recommendation bot extension 2110,and third-party bots, through the bot user interface 2010. These botscan provide suggestions based on events of the development tool 2030,which are triggered for each user action within the development tool2030. In embodiments, the bots can broadcast messages to all registeredchat clients using the API 3230, which is configured for directcommunication amongst an entire community of users.

In embodiments, the API 3230 displays all configured third-party bots.In further embodiments, a developer can select a target bot and open newconversation window. Since the API 3230 can be embedded into thedevelopment tool 2030 and can establish a communication medium with thedevelopment tool 2030, the API 3230 code should preferably be written inJavaScript.

In addition, the API 3230 is configured as a conversation-based chatwindow that can be embedded in the development tool 2030, which caninteract with various interfaces of the IDXDP, e.g., the bot userinterface 2010. The API 3230 will communicate with the NLP/machinelearning component 3290 to provide requests 3310 for material concerningnotes and comments. The API 3230 will then receive suggestions 3300 fromthe NLP/machine learning component 3290, which are relayed to the user.

As further shown in FIG. 3C, the suggestions may be in the form of aplayback video which provides an explanation for comments and forms. Inthis way, assets can be played back onto the IDE through the developmenttool 2030. In embodiments, the API 3230 can be opened as a separatewindow (popup) when a user taps on a chat icon within the developmenttool 2030. In further embodiments, tapping on the icon can toggle thevisibility of a user interface (UI) of a specific interface for the API3230, or other user interfaces, e.g., the bot user interface 2010.

FIG. 3D illustrates a computing environment 3400 implementing the API3230 and development tool 2030 with the server 3500. The computingenvironment can also be representative of the computing environmentshown in FIG. 1A. In embodiments, the API 3230 provides a mechanism tosearch and add a new bot and also list all added third party bots.Further, a default conversation window within the bot user interface2010 can interact with Kony® by Kony, Inc. bots only, and a developercan select a target bot and can open a new conversation window for eachbot through the bot user interface 2010.

The API 3230 can analyze the message response received from the server3500 and render the user interface, e.g., the bot user interface 2010,appropriately. The API 3230 is also responsible for sending/receivingthe commands to/from the development tool 2030 and supports attachingraw files, displays images, links, and text and inbuilt playback ofaudio and video. Specifically, the API 3230 can support rendering ofbuttons and choice boxes, which enables user interaction with thequeries/suggestions. Further, the API 3230 can restore the chat historyof a logged in user from previous development tool 2030 sessions, andalso allows anonymous user chat history.

The development tool 2030 comprises the user interface (UI) manager3410, which acts as a gateway from the development tool 2030 to the API3230 and the various interfaces in communication with the API 3230,e.g., the bot user interface 2010. All the communication between thedevelopment tool 2030 and the API 3230 can occur via this UI manager3410. For example, in embodiments, the UI manager 3410 creates/receivescommands to/from the development tool 2030 and the API 3230.Specifically, UI manager 3410 module has intelligence to analyze theevents flowing from the development tool 2030 and suggest actions to theuser to be performed in the context of at least the recommendationmodule 3250 and/or recommendation component 3270 as described herein. Inembodiments, the UI manager 3410 can comprise the event analytics engine2070, which comprises intelligence to analyze the events flowing fromthe development tool 2030.

As further shown, the UI manager 3410 communicates with a tasks managermodule 3420. In embodiments, the tasks manager module 3420 is a place,which stores all the tasks to be implemented in the development tool2030, which shall be called by the API 3230. The following tasks can beimplemented by the tasks manager module 3420:

1) import Project (Local/Cloud);

2) export Project (Local/Cloud);

3) create a component with or without contract, such as selected widgetinfo that should be passed to the chat window or multi widget selectionwith form;

4) export the component to the market place, such as asking the detailsabout the library name;

5) generic publishing to the market place, even when the user does notspecify the component name;

6) import from Market place, such as obtaining a mechanism to do apattern match against place components;

7) run programs, such as running maps to current development tool 2030behavior or running programs on specific channels as requested by theuser;

8) create skins, such as using a getting started model;

9) unused skins identification and deletion of them; and

10) unused actions identification and deletion of them.

The UI manager 3410 also communicates with the event processor module3430, which comprises the event queue processor 2050, queue manager2060, and event analytics engine 2070. In embodiments, the eventanalytics engine 2070 can be incorporated with the recommendation module3250 and/or recommendation component 3270 to provide analysis of theassets, actions, etc. of the user and provide appropriaterecommendations to the user.

In embodiments, the UI manager 3410 is registered to the event processormodule 3430, which further analyzes the events received from it. Inembodiments, a global actions module 3440, which, together with theglobal developer trends module 1215, is configured to obtain globaltrends within any particular industry as described with respect to FIG.7A. The development tool 2030 also includes the other modules 3450,which assist the development tool 2030 in functioning as should beunderstood by those of skill in the art such that no further explanationis required. In embodiments, all user actions in the development tool2030 are captured by the event processor module 3430.

As with all servers and AI described herein, the server 3500 usesNode.js, which is a server-side JavaScript. The bots can be implementedusing Node.js with wit.ai as the AI engine, as an illustrative example.As should be recognized by those of skill in the art, wit.ai is an openand extensible natural language platform that learns human language fromall interactions, and leverages the community such that anything that islearned is shared across developers. Third party bots can be developedby developers in any of their preferred technology areas, with the thirdparty bots being consumed seamlessly with the Node.js of the server3500. In embodiments, the server 3500 is hosted on a remote/local systemor on a cloud, i.e., cloud computing environment 1100 comprising one ormore cloud computing nodes 1110.

In embodiments, the API 3230 is automatically opened when thedevelopment tool 2030 is launched for the first time, with the API 3230preserved for future development tool startups. Specifically, uponstarting the development tool 2030, the API 3230 sends a handshakerequest to the server 3500. This handshake request shall containinformation such as:

1) user details token (if user is logged-in in a previous visualizersession);

2) internet protocol (IP) address;

3) development tool instance ID; and

4) default conversation window ID.

The server 3500 responds back with a unique token to identify the user'sdefault conversation window. In embodiments, the API 3230 will send samedetails as the initial connection for this new conversation window toserver 3500 and receives a unique token for this conversation window.The API 3230 can preserve all of the tokens from the conversationwindows and send a token with every message to the server 3500. Inembodiments where any action requires a user login, the API 3230requests user login before executing the action in order to have theability to complete the action.

The server 3500 comprises an autonomous bot 3520, third-party bot(s)3530, market place bot 3540 and help bot 3550 (amongst other botsdescribed herein), which all communicate with the bot user interface2010. The autonomous bot 3520 provides commands to the development tool2030, such as build code, import/export component, etc. The third-partybot(s) 3530 includes at least two types of third-party chat bots. Forexample, the third party bots include chat bots built for the IDXDP,where these bots are directly added to IQ manager 3510 using an adminconsole. The developers can communicate with these bots once they add abot to their API 3230 through an interface such as the bot userinterface 2010, which can understand the requests to the bots from theintelligence, e.g., AI and NLP, within the bot AI/NLP 2020. A second botincludes chat bots already built for other providers. If required, adeveloper can write a connector, which makes these chat bots work withthe IDXDP as should be understood by those of ordinary skill in the art.

The market place bot 3540 is a chat bot that helps with the market placecomponent search and make appropriate suggestions. The help bot 3550 isa chat bot that provides help information consolidated from Kony®documentation, forums and the local or global community. The help bot3550 can respond with detailed help with images, video, text, links andsample code as detailed with respect to real-time collaboration andtutorials as described herein.

In embodiments, an application developer or other user can communicatedirectly with the Development tool 2030 or with the server 3500 throughvarious interfaces, e.g., the bot user interface 2010. For example, adeveloper may choose to issue a command to the development tool 2030directly from the API 3230 by typing the command within the bot userinterface 2010. Specifically, the user may type “Convert the selectedFlex to master” into the API 3230 through the bot user interface 2010,which can be recognized by the intelligence of the bot AI/NLP 2020. Inthis scenario, the raw text entered into the Visualizer API 3230 followsa “Direct Communication with bots” approach to interact with theautonomous bot 3520. The autonomous bot 3520 can then respond with anaction message through the bot user interface 2010 to the API 3230.Specifically, this action is analyzed by API 3230, which sends thecommands to development tool 2030. The UI manager 3410 insidedevelopment tool 2030 receives the command and executes the command inthe development tool 2030 using the tasks manager module 3420.

Alternatively, a developer may communicate directly with the server3500, and specifically with the informative bots within the server 3500through the bot user interface 2010. These cots can be any of the botsas described herein. In embodiments, an application developer can type araw message into the conversation window e.g., bot user interface 2010in communication with the API 3230. The Visualizer API 3230 convertsthis raw message using the intelligence provided by the bot AI/NLP 2020into a predefined template request and sends this request to the server3500.

The request is received by the IQ manager 3510, which sends the requestto the appropriate chat bot, i.e., autonomous bot 3520, third-partybot(s) 3530, market place bot 3540 or help bot 3550. In embodiments, thechat bot can apply NLP, if required. The chat bot responds with intentsor commands in a predefined response format/template. The intents orcommands are passed back to the user's chat window by the IQ manager3510. The API 3230 analyzes the response template and displays it in theappropriate format, e.g., spoken, visual, or textual, to the developer.The API 3230 can also broadcast messages to all registered users. Inembodiments, the IQ manager 3510 can identify all available users andsend the same message to all the users.

The requests and messages provided to and by the API 3230 can be in anumber of formats. For example, a body of a hypertext transfer protocol(HTTP) request can be sent in JSON format, requiring the properties ofmessaging_type, recipient and message. In embodiments, themessaging_type property provides the purpose of the message being sent,the recipient property identifies the intended recipient of the message,and the message property defines the message to be sent. Examples of themessaging_type property can be a message in response to a receivedmessage, or a message that is being sent proactively and not in responseto a received message. Examples of the recipient property include apage-scoped user identification (PSID) of the message recipient.Optionally, the recipient property can be a user name from any thirdparty cloud, e.g., the cloud computing environment 1100 comprising oneor more cloud computing nodes 1110 as shown in FIG. 1B. Examples of themessage property include a message text or an attachment object.Properties can also include a message state displayed to the user, withthe properties of typing_on and typing_off.

The typing_on property indicates a display of the typing bubble, whiletyping_off property indicates a removal of the typing bubble. In furtherembodiments, there can be an optional push notification having varioustypes. The push notification type can be regular, which is representedby sound. Alternatively, the push notification type can be asilent_push, which is represented by an on-screen notification only, ora no_push, which is represented by no notification. The regular pushnotification type is the default.

In embodiments, an attachment object is a component used to sendmessages with media or structured messages, where the text or attachmentis set. The following can be included in the attachment object: richmedia messages including images, audios, videos, or files; and templatesincluding generic templates, button templates, receipt templates, orlist templates, etc. Attachment types include audio, video or imagefile. To send an attachment from a file, a POST request can be submittedto a Send API with the message details as form data, with the followingfields:

recipient: A JSON object identifying the message recipient;

text: an optional text message to send with the asset;

message: A JSON object describing the message. Includes the asset type,and a payload. The payload is empty; and

filedata: The location of the asset on your file system andmulti-purpose internet mail extensions (MIME) type.

For attaching a template, the body of the request can follow a standardformat for all template types, with a message.attachment.payloadcomponent containing the type and content details that are specific toeach template type. The available templates include generic templates,list templates button templates, receipt templates, media template, codetemplates, amongst other examples.

Generic Templates:

The generic template allows a user to send a structured message thatincludes an image, text and buttons. For example, a generic templatewith multiple templates described in the elements array will send ahorizontally scrollable carousel of items, each composed of an image,text and buttons. For the message.attachment.payload component of thegeneric template, the template_type property value may be generic. Theimage_aspect_ratio property is optional and represents an aspect ratioused to render images that can be specified by element.image_url. Theelements property represents an array of element objects that describeinstances of the generic template to be sent. In embodiments, specifyingmultiple elements will send a horizontally scrollable carousel oftemplates. In embodiments, the generic template supports a maximum of 10elements per message, although other elements are also contemplatedherein.

The message.attachment.payload.elements component of the generictemplate can include a title property with an 80-character limit (as anon-limiting example) that is to be displayed in the template. Asubtitle property is optional and can also have an 80-character limit(as a non-limiting example) that is to be displayed in the template canbe included. Other optional elements for themessage.attachment.payload.elements component of the generic templateinclude the URL of the image to display in the template, and a defaultaction executed when the template is tapped. The default action can beprovided by the following command:

{ “type”: “web_url”, “unl”: “<URL_TO_OPEN_IN_WEBVIEW>”, }Another optional property is a buttons property, which represents anarray of buttons to append to the template. A maximum of 3 buttons perelement is supported, as an example.

List Template:

The list template allows a user to send a structured message with a setof items rendered vertically. For the message.attachment component, thevalue property can be a template with the payload property representingthe payload of the template. For the message.attachment.payloadcomponent, the value property can be list. An optional property is thetop_element_style property, which sets the format of the first listitems. The messenger web client can render compact, where compactrenders a plain list item and large renders the first list item as acover item. Also, the buttons property is optional, and represents thebutton to display at the bottom of the list. Maximum of 1 button issupported, as a non-limiting example.

The elements property of the message.attachment.payload componentrepresents an array of objects that describe items in the list, with aminimum of two elements required (as a non-limiting example) and maximumof four elements supported (as a non-limiting example). For themessage.attachment.payload.elements component, the title propertyrepresents the string to display as the title of the list item, with an80-character limit (as a non-limiting example). In embodiments, thetitle may be truncated if the title spans too many lines, and must alsohave one or both of image_url or subtitle set. The subtitle property isoptional and represents a string to display as the subtitle of the listitem and an 80-character limit as a non-limiting example. Inembodiments, the subtitle may be truncated if the subtitle spans toomany lines. The subtitle and should also have one or both of image_urlor subtitleset. The image_url property is also optional and representsthe URL of the image to display in the list item. Further, the defaultaction property is optional, with a URL button that specifies thedefault action to execute when the list item is tapped.

Button Template:

The button template includes the message.attachment component that has avalue property, which could be a template. Further, the payload propertyrepresents a payload of the template. For the message.attachment.payloadcomponent, the value property must be button. The text property of themessage.attachment.payload component can be UTF-8-encoded text of up to640 characters as a non-limiting example, with the text appearing abovethe buttons. For the buttons property of the message.attachment.payloadproperty, a set of 1-3 buttons that appear as call-to-actions.

Receipt Template:

The receipt template allows a user to send an order confirmation as astructured message. The message.attachment component includes a valueproperty, which must be template, while the payload property representsa payload of the template. The message.attachment.payload componentincludes the template_type property where the value must be receipt. Therecipient_name property represents the recipient's name. Themerchant_name property is optional, and, if present, is shown as logotext. The order_number must be unique, while the currency of themessage.attachment.payload property represents the currency of thepayment.

The payment_method represents the payment method. Providing informationfor the customer to decipher which payment method and account they canuse includes, for example, a custom string, such as, “creditcard name1234”. The timestamp property is optional and is in the order inseconds. The elements property of the message.attachment.payloadcomponent represents an array of a maximum of 100 element objects (as anon-limiting example) that describe items in the order. Sort order ofthe elements is not guaranteed. The address property is optional andrepresents the shipping address of the order. The summary propertyrepresents the payment summary. The adjustments property is optional andrepresents an array of payment objects that describe paymentadjustments, such as discounts.

For the message.attachment.payload.address component of the receipttemplate, the street_1 property represents the street address, line 1.The street_2 property is optional and represents the street address,line 2. The city property represents the city name of the address, whilethe postal_code property represents the postal code of the address, thestate property represents the state abbreviation for U.S. addresses, orthe region/province for non-U.S. addresses, and the country propertyrepresents the two-letter country abbreviation of the address.

For the message.attachment.payload summary component of the receipttemplate, the property values of the summary object should be valid,well-formatted decimal numbers, using ‘.’ (dot) as the decimalseparator. It is noted that most currencies only accept up to 2 decimalplaces. The subtotal property is optional and represents the sub-totalof the order. Also optional is the shipping_cost property, whichrepresents the shipping cost of the order, and the total_tax property,which represents the tax of the order. The total_cost propertyrepresents the total cost of the order, including sub-total, shipping,and tax. For the message.attachment.payload.adjustments component of,the name property is optional and represents a name of the adjustment.The amount property is also optional and represents the amount of theadjustment.

For the message.attachment.payload.elements component, the titleproperty represents the name to display for the item. The subtitleproperty and the quantity property are both optional, with the subtitleproperty representing the subtitle for the item, usually a brief itemdescription, and the quantity property representing the item purchased.The price property represents the price of the item. For free items, a‘0’ is allowed. The currency property and the image_url property areboth optional, with the currency property representing the currency ofthe item price and the image_url property representing the URL of animage to be displayed with the item.

Media Template:

The media template allows a user to send a structured message thatincludes an image or video, and an optional button. Themessage.attachment component of the media template includes a typeproperty where the value is template. The payload property is thepayload of the template. The message.attachment.payload component of themedia template includes a template_type property, where the value mustbe media. The elements property of message.attachment.payload componentrepresents an array containing 1 element object that describes the mediain the message. The message.attachment.payload.elements component of themedia template includes a media_type property, which represents the typeof media being sent—image or video is supported. Further, the URLproperty represents a URL of the image, and the buttons propertyrepresents an array of button objects to be appended to the template. Amaximum of 1 button is supported.

Code Template:

The code template includes the message.attachment component, whichincludes a type property where the value must be template, and a payloadproperty which is a payload of the template. Themessage.attachment.payload component of the code template has atemplate_type property where the value must be code and the code is aJavaScript code to be displayed.

Buttons are defined by objects in a buttons array, with messagetemplates supporting buttons that invoke different types of actions.Examples of buttons include a postback button, a URL button a callbutton, a share button, a buy button, log in button, a log out button,and a game play button. When the postback button is tapped, a messengerplatform can send an event to a user's postback webhook. This is usefulwhen a user wants to invoke an action in their selected bot. This buttoncan be used with the button template and the generic template.Properties of the postback button include a type, which represents thetype of button. Another property is a title, which represents a buttontitle with a 20-character limit as a non-limiting example. A furtherproperty of the postback button is a payload, which represents data thatwill be sent back to the user's webhook. The payload has a1000-character limit.

The URLbutton opens a webpage in the messenger webview. This button canbe used with the button and generic templates. The properties of the URLbutton include a type, which represents a type of button and must beweb_url. The title represents the button title and has a 20-characterlimit. The URL property represents the URL that is opened in a digitalbrowser when the button is tapped and must use hypertext transferprotocol secure (HTTPS) if the messenger_extensions is true. Thewebview_height_ratio is optional and represents a height of the webview.Valid values are compact, tall and full, with defaults to full.

The messenger_extensions property is optional and needs to be true ifusing Messenger Extensions. The fallback_url property represents the URLto use on clients that do not support Messenger Extensions. If this isnot defined, the URL will be used as the fallback. It may be specifiedif messenger_extensions is true. The webview_share_button is optional,and is set to hide to disable the share button in the Webview (forsensitive info). This does not affect any shares initiated by thedeveloper using Extensions.

Quick Replies allow for obtaining message recipient input by sendingbuttons in a message. When a quick reply is tapped, the value of thebutton is sent in the conversation, and the messenger platform sends amessages event to the user's webhook. The properties of the quickreplies include a content_type, which must be text: sends a text button,or location: sends a button to collect the recipient's location. Anotherproperty is title, which represents the text to display on the quickreply button with a 20-character limit as a non-limiting example. Thisis required if a content_type is ‘text’. The property of payloadrepresents custom data with 1000-character limit (as a non-limitingexample) that will be sent back via a messaging_postbackswebhook event.The payload is required if content_type is ‘text’. The image_urlproperty is optional and represents the URL of an image to display onthe quick reply button for text quick replies. In embodiments, the imageshould be a minimum of 24 px×24 px. Larger images will be automaticallycropped and resized.

FIG. 3E represents the IQ manager 3510, which communicates with chatclients, i.e., the API 3230, and the chat bots, e.g., the autonomous bot3520, third-party bot(s) 3530, market place bot 3540 and help bot 3550.The IQ manager 3510 comprises a Messaging manager 3555, a Chat Historymanager 3560, a bot manager 3565, an Identity manager 3570, a clientmanager 3575 and admin console manager 3580. In embodiments, the UImanager 3410 acts as a gateway to the IQ manager 3510.

In embodiments, the IQ manager 3510 authenticates the user by using anidentity service module of the Identity manager 3570. In embodiments,this can be the same authentication as is currently used in the KonyCloud™ of Kony, Inc. user authentication or other cloud computingenvironment, e.g., cloud computing environment 1100. In embodiments, theIdentity manager 3570 analyzes the user details, IP address, andconversation window details and produces a unique token to identify thechat client, i.e., the user. The IQ manager 3510 provides a uniqueidentification (ID) to each registered bot, e.g., autonomous bot 3520,third-party bot(s) 3530, market place bot 3540 and help bot 3550, witheach request and response containing both chat client token and the botID. In embodiments, the information of the registered bots' is saved asmeta info (typically JSON), which is used by the IQ Manger 3510 togenerate the same ID. Further, all the registered bots can communicatewith the IQ manager 3510.

By default, all messages are routed to Kony® chat bots; althoughcommunications can be provided to any of the chat bots as desired by theadministrator. For example, if a developer selects a third-party chatbot conversation window, the messages can be routed to the correspondingthird-party chat bot only. The chat history of all users for allconversation windows can be monitored and recorded by the Chat Historymanager 3560. The bot manager 3565 is responsible for preserving theexisting bot information and providing the same to the IQ manager 3510for the communications. The client manager 3575 primarily deals withstoring the client information, which includes both active and inactiveuser sessions. This information can be stored in the storage system1022B as shown in FIG. 1A, for example.

The admin console manager 3580 can be a web page or other interface fromwhich admin settings can be configured in the server 3500. The adminconsole manager 3580 can be responsible for, amongst other functions:

1) registering a new bot (Kony′ or third party) with the IQ manager3510;

2) unregistering an existing bot;

3) disabling a bot, where the bot can be public or enterprise. It canalso be private bot during development phase;

4) listing out existing client sessions. (both active and inactivesessions); and

5) viewing and clearing chat history.

FIG. 3F illustrates a swim diagram 3800 for implementing processes inaccordance with aspects of the present disclosure. In embodiments, thesub-systems or actors can include the development tool 2030 and relatedmodules (modules of Kony Visualizer® by Kony, Inc), the queue manager2060 and the recommendation engine 2090. As the user performs actions inthe development tool 2030, the actions are collected and the informationprovided by the actions are then utilized to determine an intent of theuser and provide a recommendation based on the intent. For example, theevent queue manager, i.e., queue manager 2060 is responsible forproviding meaningful event information coupled with context from thedevelopment tool 2030 in a structured object notation. The IQrecommendation engine, i.e., the recommendation engine 2090, shallprocess the events and provides suggestions to developers or otherusers. The recommendation engine 2090 registers itself as a subscriberto all the events raised by various sub-systems of the development tool2030. In the process, it collects the information in JSON format. Theinformation collected includes:

1) user actions to create, rename, delete forms, skins, widgets etc.;

2) corresponding actions/events raised by various sub systems with inthe development tool 2030;

3) captures information like the name of the form, skin, specific useractivity, channel that user is building the application for;

4) additional contextual information about the application, e.g., appname, domain, etc.;

5) the information so captured is stored in a NoSQL database; and

6) JavaScript Code changes by users.

As shown in FIG. 3F, the development tool 2030 registers as a source ofall events, i.e., user actions, at step 3810. At step 3820, the queuemanager 2060 subscribes to the recommendation engine. At step 3830, thedevelopment tool 2030 raises, i.e., fires, the events. This causes atriggering of events with structured objects at step 3840, i.e.,components being constructed in response to user actions. With thisinformation, the recommendation engine 2090 analyzes the events,prepares suggestions and converts those suggestions into naturallanguage statements at step 3850. In embodiments, the suggestions 3850can comprise a recommendation context specific help, amongst otherexamples.

Autonomous Advisor

As described herein, the IDXDP may include an autonomous advisorfunctionality that is implemented, for example, using the autonomousadvisor module 1210 described with respect to FIG. 1C. In accordancewith aspects of the present disclosure, the autonomous advisorfunctionality automatically determines an intent of a user who isworking in the IDE, and automatically makes recommendations to the userfor actions to take in the IDE, where the recommendations are based oncomparing the user's intent to insights determined from big data usinganalytics and machine learning.

For example, as described with respect to FIG. 2A, modules of the system(e.g., the event analytics engine 2070 and the event AI/NLP engine 2080)may be configured to determine an intent of a user working in the IDE2030 based on applying cognitive analysis and AI techniques to theevents that the user performs in the IDE 2030. Moreover, portions of thesystem (e.g., the event AI/NLP engine 2080) may be configured todetermine insights about the ways that other users are performing tasksin the IDE 2030 by continuously obtaining and analyzing big data usingcognitive analysis and AI techniques. The determined insights mayinclude, for example, patterns, correlations, trends, and preferencesassociated with ways that others are using the IDE 2030. In embodiments,the recommendation component 3270 (e.g., the recommendation engine 2090)uses the insights to automatically generate recommendations that thesystem provides via the bot user interface 2010 to the user working inthe IDE 2030. In this manner, implementations of the invention may makeintelligent recommendations to a user by: (i) determining the user'sintent in the IDE; (ii) comparing the user's intent to determined waysthat other users are performing the same or similar tasks in the IDE;and (iii) recommending to the user that the user accomplish their intentby using one of the determined ways that other users are performing thesame or similar tasks in the IDE.

In embodiments, the big data used in determining the insights includesinternal data (e.g., platform data) gathered from other users workingwith instances of the IDE that are connected to the IDXDP. For example,the big data may include data obtained from respective IDEs of pluralother users (potentially worldwide) who are all using the platform(e.g., the Kony® platform of Kony, Inc., including Kony Visualizer®).For example, the system may be configured to continuously monitor andtrack every action (e.g., every keystroke, mouse input, etc.) taken byevery user in their respective IDE, and to provide this data to theIDXDP for analysis as part of the big data. Such actions may include,for example and without limitation: menu items in the IDE that the userselects; text typed by the user in a field in the IDE; and names appliedby a user to objects, widgets, forms, etc., in a project in the IDE. Theinternal data may also include apps that are created using the IDE andthat are published to a marketplace (e.g., the Kony Marketplace™ byKony, Inc.).

The big data may also include data that is not generated by an IDEassociated with the IDXDP. For example, the big data may includeexternal data (e.g., community data) such as: social media sources (usersocial media posts, comments, follows, likes, dislikes, etc.); socialinfluence forums (e.g., user comments at online blogs, user comments inonline forums, user reviews posted online, etc.); activity-generateddata (e.g., computer and digital device log files including web sitetracking information, application logs, sensor data such as check-insand other location tracking, data generated by the processors foundwithin vehicles, video games, cable boxes, household appliances, etc.);Software as a Service (SaaS) and cloud applications; and transactions(e.g., business, retail, etc.). Such external data may be obtained usingdata mining, web scraping, etc., of publicly available (e.g., Internet)data and/or enterprise data from other third party applications.

In accordance with aspects of the present disclosure, the system usescognitive analysis techniques (e.g., NLP, sentiment analysis, etc.) andAI techniques (e.g., machine learning, neural networks, etc.) to analyzethe big data to determine insights about how users are performing tasksin the IDE. For example, the system may analyze the internal data ofthousands of worldwide users of the IDE and learn from this analysisthat when a user performs a specific set of events in the IDE (e.g., aseries of mouse clicks, typing, drag and drops, etc., in the IDE), theuser is creating a particular object (e.g., an authentication object fora digital app). As another example, the system may analyze external dataincluding blog posts, comments, and ratings of published digital appsand may learn from this analysis that the current best practice forpermitting a user to select between two options in a digital app is touse a visual toggle switch (that requires a single input) instead of adrop down menu (that requires at least three inputs). These two examplesare merely for illustration, and implementations of the invention may beused to determine any number of and any type insights (e.g., patterns,correlations, trends, preferences, etc.) about using the IDE.

The system is not limited to analyzing the internal data separately fromthe external data, and instead both internal data and external data maybe included in a set of big data that is analyzed to determine insights,which may include patterns, correlations, trends, and preferencesassociated with using the IDE. Moreover, the big data is not static, butinstead is ever growing due to the system continuously obtaining newdata (e.g., both internal and external) as it becomes available. In thismanner, since the big data changes over time, any determined insights(e.g., patterns, correlations, trends, preferences, etc.) may alsochange over time as a result of the change in the big data. As a result,something that is determined as a best practice today (e.g., using avisual toggle switch instead of a drop down menu) may be superseded by adifferent best practice determined from the big data in the future.

In embodiments, the analysis of the big data may be performed by theevent AI/NLP engine 2080 as described with respect to FIG. 2A. Big data,by definition, involves data sets that are so large or complex thattraditional data processing application software is incapable ofobtaining and analyzing the data. As such, it follows that the eventAI/NLP engine 2080 is necessarily rooted in computer technology sincethe processes involved are impossible to perform without computertechnology (i.e., the processes involved in obtaining and analyzing bigdata cannot be performed in the human mind). In embodiments, the eventAI/NLP engine 2080 may include a plurality of computer devices (e.g.,servers) arranged in a distributed network (e.g., a cloud environment).

In embodiments, autonomous recommendations as described herein arepresented to the user of the IDE via the bot user interface 2010 of FIG.2A. The system may present the recommendation in a visual form (e.g., achat box with text, images, etc.) and/or or audible form (e.g., usingtext to speech and an audio speaker). Embodiments in accordance withaspects described herein may be implemented in the cloud computingenvironment 1100 of FIG. 1B, with the architecture 2000 of FIG. 2Acomprising one or more cloud computing nodes 1110 as described withrespect to FIG. 1B, plural user computer devices communicating with thearchitecture 2000 (e.g., accessing the IDE 2030) comprising plural othercloud computing nodes 1110, and plural computer devices from which theexternal data (e.g., community data) is obtained comprising still othercloud computing nodes 1110.

As described herein, autonomous recommendation (or a recommendation madeautonomously) refers to a recommendation the system presents to the userwithout having been solicited by the user. This stands in contrast tointelligent advisor systems in which the user first asks for help orassistance, and the systems only provide a recommendation in response tosuch a user request for help or assistance. All of the recommendationsdescribed in this section are autonomous recommendations unlessexplicitly stated otherwise.

This section describes a number of examples of autonomousrecommendations that may be provided with implementations of the presentdisclosure, including: customized recommendations based on determineduser persona; repetitive action recommendations; shortcutrecommendations; automatic completion recommendations; style advisorrecommendations; style evaluator recommendations; padding managerrecommendations; image advisor recommendations; design time testingrecommendations; app testing recommendations; defect trackingrecommendations; workflow assistant recommendations; standards adherencerecommendations; best practices recommendations; DevOps advisorrecommendations; refactoring recommendations; performancerecommendations; code cleanup and linting recommendations; upselladvisor recommendations; automated client success recommendations;automated Mobile Test Automation (MTA) recommendations; and featurerequest (FTR) advisor recommendations. These examples are not intendedto be limiting, and implementations of the invention may be used to makeother types of autonomous recommendations to users of the IDE based oninsights determined from big data.

Customized Recommendations Based on Determined User Persona

In accordance with aspects of the present disclosure, the autonomousadvisor functionality determines a persona of a user and customizes therecommendations described herein based on the determined persona ofuser. Different users may act under different personas (e.g., roles)within an enterprise when working in the IDE, and the system maycustomize the vocabulary, complexity, and/or frequency of arecommendation based on the different personas. For example, a firstuser may be a designer who has a relatively low understanding of coding,and a second user may be a developer who has a relatively high level ofunderstanding of coding. In this example, when generating arecommendation for the first user, the system would avoid usingreferences to coding vocabulary and coding concepts since the first useris likely to not understand such a recommendation. On the other hand,when generating a recommendation for the second user in this example,the system would include coding vocabulary and coding concepts in therecommendation.

In embodiments, the system determines the user's persona either from anindication by the user of by analyzing a user's actions in the IDE. Inone method, a user may indicate their persona in a user profileassociated with the IDE. In this implementation, when the user logs into the IDE with their credentials, the system accesses the user profileassociated with these credentials and determines the persona of thisuser from the data in the user profile. The IDE may be configured suchthat a user can select from a set of predefined personas when editingtheir profile in the IDE. The system may use the persona determined inthis manner when customizing recommendations in the manner describedherein.

Another method of determining a persona of a user is for the system toprompt the user while the user is working in the IDE. For example, whena user logs in to the IDE with their credentials, the system may promptthe user (e.g., visually and/or audibly) to provide user input toindicate their current persona. In this manner, the system provides theuser with the ability to indicate a different persona each time they login to the IDE, which can be helpful for users who perform differentroles at different times within the enterprise. The system may use thepersona determined in this manner when customizing recommendations inthe manner described herein.

Another way of determining a user persona is for the system to analyzethe actions the user performs in the IDE and to designate a persona forthe user based on this analysis. For example, the system may store datathat defines different actions in the IDE as being associated withdifferent ones of the available personas. In embodiments, a module ofthe system (e.g., the event analytics engine 2070) analyzes the actions(e.g., events) performed by the user in the IDE 2030, compares theseactions to the data that defines different actions in the IDE as beingassociated with different ones of the available personas, and determinesa persona for the user based on this comparison. This described methodof analysis is merely illustrative, and other methods of automaticallydetermining a user's persona may be employed. In this manner, the systemmay automatically determine a persona for a user based on actionsperformed by the user in the IDE. The system may use the personadetermined in this manner when customizing recommendations in the mannerdescribed herein.

FIGS. 4A and 4B illustrate the system providing differentrecommendations for different users based on the different determinedpersonas of the users. For example, FIG. 4A shows a workspace 4010(e.g., a UI in the IDE 2030 of FIG. 2A) of a first user that the systemdetermines has the persona of a developer. On the other hand, FIG. 4Bshows an illustrative workspace 4010′ (e.g., a UI in the IDE 2030) of asecond user that the system determines has the persona of a designer. Inthese workspaces, each of the users is manually drawing an object 4005and 4005′.

Using methods described herein with respect to the autonomous advisorfunctionality, the system automatically determines the intent of eachuser, i.e., drawing an object. Using methods described herein withrespect to the autonomous advisor functionality, the systemautomatically determines a recommendation for each user based on thedetermined intent, and provides the recommendation 4015 and 4015′ to theusers via a chat window (e.g., as part of the bot user interface 2010 ofFIG. 2A). The core of the recommendation is the same for each user,e.g., that images can be imported from another application instead ofthe user manually drawing images in the workspace. However, the contentof each recommendation is customized based on the different persona ofeach respective user.

For example, as shown in FIG. 4A, the recommendation 4015 to the firstuser is customized to the persona of a developer by making therecommendation 4015 concise (e.g., based on a presumption that adeveloper knows how to import images without instruction). As shown inFIG. 4B, the recommendation 4015′ to the second user is customized tothe persona of a designer by making the recommendation 4015′ moreelaborate (e.g., based on a presumption that a designer would benefitfrom step by step guidance for importing images). In this manner, therecommendations for the two different users are customized to usedifferent complexity based on the different personas.

Recommendations may also be customized based on other factors, such asthe frequency with which a particular recommendation is made. Using theexample of FIGS. 4A and 4B, the system may further customize therecommendation 4015 for the first user (i.e., the developer) by showingthis recommendation only once even if the system determines the sameintent from this same user in the future (e.g., based on a presumptionthat a developer does not want to be told multiple times). Similarly,the system may further customize the recommendation 4015′ for the seconduser (i.e., the designer) by showing this recommendation every time thesystem determines this same intent by the user (e.g., based on apresumption that a designer would benefit from this repeatedinformation). In this manner, the recommendations for the two differentusers are customized to use different frequencies based on the differentpersonas.

Other types of customization based on personas may be used. For example,the system may use different vocabularies for different personas.Specifically, the system may employ jargon and/or code syntax inrecommendations to developers, while avoiding including jargon and/orcode syntax in recommendations to designers.

In an illustrative example, the system may determine a user to have apersona selected from the group consisting of: designer, developer,business owner, sales, marketing, and platform. These exemplary personasare not intended to be limiting, however, and any number and any typesof personas may be defined by in implementations of the invention foruse in customizing recommendations to users.

In embodiments, rules that define how to customize different types ofrecommendations for each of the different types of personas may bestored and used by the system when determining how to customize arecommendation for a particular user. The rules described in the exampleof FIGS. 4A and 4B (e.g., a presumption that a developer knows how toimport images without instruction, and a presumption that a designerwould benefit from step by step guidance for importing images) aremerely illustrative, and any types and any number of rules may bedefined for use by the system in customizing the recommendations.

Autonomous Recommendations Based on Repetitive Actions

In accordance with aspects of the present disclosure, the autonomousadvisor functionality provides autonomous recommendations to a userbased on determining the user is performing repetitive actions that canbe performed in a more efficient manner In embodiments and as previouslydescribed herein, the system continuously monitors the actions made bythe user in the IDE (e.g., the event data). In embodiments, the systemanalyzes the event data to determine repetitive actions being performedby a user in the IDE, and automatically (or with user permission afterprompt) refactors those repetitive actions into specific applicationcomponents. The specific application components can include but are notlimited to forms, master templates (e.g., masters), macros, widgets,services, etc.

As described herein, and with reference to the elements and architectureshown in FIG. 2A, the event queue manager 2060 manages communicationsbetween different modules of the IDE 2030 via events, and provides eventinformation coupled with IDE context information in a structured objectnotation. As additionally described herein, the system continuouslymonitors every event (e.g., action) that a user performs in the IDE andanalyzes the stream of events (e.g., using the event analytics engine2070) to determine an intent of the user.

According to aspects of the present disclosure, the event analyticsengine 2070 is configured to analyze the event data from the event queuemanager 2060 to determine when a user is performing a repetitive actionin the IDE. When the event queue manager 2060 determines a user isperforming a repetitive action in the IDE, and when the determinedinsights reveal there is a less repetitive approach that satisfies theuser's determined intent, then the recommendation engine 2090 makes arecommendation to the user via the bot user interface 2010 to use theless repetitive approach that satisfies the user's determined intent.

For example, the system may determine (e.g., from analyzing the user'sevent data from the event queue manager) that the user's intent iscreating a scrollable form that implements product level detail, e.g.,for an online shopping functionality of a digital app. In particular,the system may determine from the event data that the user has manuallycreated source code for each of ten horizontal rows on each of threepages, where each row has the same type of widgets (e.g., an imagewidget, a ratings widget, and a product description widget). The systemmay determine from the insights that a less repetitive approach tosatisfy this intent is to use a SegmentedUI widget that is available inthe IDE and that includes multiple segments (rows or records), in whicheach segment (row or record) can have multiple child widgets, and whichis adapted for menus and grouped lists. Based on these determinations,the system makes a recommendation to the user to use the SegmentedUIinstead of manually creating the repetitive components of the scrollableform.

FIG. 4C shows a swim lane diagram illustrating a method in accordancewith aspects of the present disclosure that are directed to autonomousrecommendations based on repetitive actions. The steps of FIG. 4C may becarried out in the architecture of FIG. 2A and are described withreference to elements shown in FIG. 2A.

At step 4021, the IDE 2030 registers with the event queue manager 2060as a source of all events in the event stream. At step 4022, therecommendation engine 2090 registers itself as a subscriber to all theevents raised by the IDE 2030 (e.g., raised by the various modulesand/or subsystems of the IDE). At step 4023, the event queue manager2060 raises event “x” from the IDE 2030. At step 4024 and in response tostep 4023, the event queue manager 2060 triggers event “x” to therecommendation engine 2090 including structured objects such as form,widget, service, code snippet, etc. At step 4025, the event queuemanager 2060 again raises event “x” from the IDE 2030. At step 4026 andin response to step 4025, the event queue manager 2060 triggers event“x” to the recommendation engine 2090 including structured objects suchas form, widget, service, code snippet, etc. At step 4027, therecommendation engine 2090 analyzes the repetitive sequential events(e.g., event “x”), and makes a recommendation to the user for adifferent approach that does not involve as many repetitive events. Asdescribed herein, the different approach may be determined based on thedetermined intent of the user and the insights determined from the bigdata. As described herein, the recommendation may be presented to theuser in the IDE 2030 via the bot user interface 2010.

Autonomous Recommendations for Shortcuts

In accordance with aspects of the present disclosure, the autonomousadvisor functionality provides autonomous recommendations for shortcutsto a user. In embodiments and as previously described herein, the systemcontinuously monitors the actions made by the user in the IDE (e.g., theevent data). Based on this monitoring, the system is configured todetermine when a user repeats an action with a number of events when ashortcut (e.g., to perform the same action with less events) isavailable to be used. Based on this determining, the systemautomatically presents a recommendation to the user to use the shortcut.

Sometimes there are a plurality of different ways to perform the samefunction in the IDE. For example, a first approach for performing acertain task may involve the user performing a series of keystrokes andmouse input actions for navigating through the UI of the IDE. A secondapproach for performing the same task may involve the user initiating amacro programmed into the UI of the IDE, where the second approach is ashortcut because it involves less user input to perform the same task.In accordance with aspects of the present disclosure, the systemmonitors and analyzes the event data (e.g., from the event queue manager2060) to determine when the user is repeatedly performing the firstapproach. In response to determining that the user is repeatedlyperforming the first approach, the system (e.g., the recommendationengine 2090) automatically generates a recommendation to the user tosuggest using the second approach. As described herein, therecommendation may be presented to the user in the IDE 2030 via the botuser interface 2010.

Autonomous Recommendations to Automatically Complete

In accordance with aspects of the present disclosure, the autonomousadvisor functionality provides autonomous recommendations toautomatically complete actions for a user based on the determined intentof the user and the determined insights. In embodiments, the systemrecommends completing a user's action with a similar action that hasbeen performed by another user.

As described herein, the system continuously monitors every event (e.g.,action) that a user performs in the IDE and analyzes the stream ofevents (e.g., using the event analytics engine 2070) to determine anintent of the user. The intent may be, for example, to create a portionof a digital app such as a splash screen, an authentication field, etc.The intent may be determined before the user has performed all theactions to achieve the intent, in which case the system may determine(from the insights) how other users have achieved this same intent, andsuggest one or more of these existing solutions to the current user.

FIGS. 4D, 4E, and 4F illustrate this functionality. As shown in FIG. 4D,the user is creating a calculator 4030 in their workspace 4035 in theIDE, the calculator 4030 being only partially complete. Using thetechniques described herein (e.g., analyzing the event data from the IDEusing cognitive analysis and AI), the system may determine that theuser's intent is to create a calculator. As shown in FIG. 4D, based ondetermining the user intent, the system may present a recommendation4040 to the user to see examples of calculators. The recommendation 4040may have fields for the user to provide input to accept or decline therecommendation.

In response to the user providing input to accept the recommendation4040, then as shown in FIG. 4E the system may modify the recommendation4040′ to include images (e.g., thumbnails) 4045, 4046 of completedcalculators that have been made by other users. The system may find thecompleted calculators in the big data (e.g., in the internal data ofother users of the IDE), and may use the insights to determine which oneor more completed calculators best matches this user's intent.

For example, the system may determine that plural apps published in amarketplace each include a calculator, and the system may obtain andanalyze data about these plural apps in order to determine which one ormore of the calculators to present to the user in the recommendation4040′. As an illustrative example, such data might include: ratings data(e.g., number of stars out of five) of each of the apps from themarketplace; review data (e.g., user comments in text form) of each ofthe apps from the marketplace; similarity of appearance of each of thecalculators (e.g., in the respective plural apps) to the calculatorstarted and partially completed by the user (e.g., by comparingdimensions, spatial relations, colors, fonts, functions, etc., ofobjects, widgets, forms, etc., included each of in the calculators);social media posts that tag, like, dislike, thumbs-up, thumbs-down, orplus-one an image and/or a textual description of the calculatorincluded one of the plural apps; and articles, blogs, forum discussionsthat include an image and/or a textual description of the calculatorincluded one of the plural apps. Based on analyzing this data (orsimilar data) in the manner described herein (e.g., using cognitiveanalysis and/or AI techniques), the system may determine a relativescore for each calculator in the respective plural apps, and may presenta predefined number of the highest scoring calculators to the user inthe recommendation 4040′.

As shown in FIG. 4F, based on the user selecting the image 4045 in therecommendation 4040′, the system automatically replaces the user'spartially complete calculator 4030 with a calculator 4050 thatcorresponds to the selected image 4045. In embodiments, the system isconfigured to automatically execute commands in the IDE to generate theobjects included in the calculator 4050, such that the calculator 4050is functional in the IDE in the same manner as if the user hadconstructed the calculator 4050 themselves in the IDE. In this manner,aspects of the present disclosure provide autonomous recommendations toautomatically complete actions for a user based on the determined intentof the user.

Autonomous Recommendations to Revise

In accordance with aspects of the present disclosure, the autonomousadvisor functionality provides autonomous recommendations to a userbased on determining that a more appropriate component may be used in aproject. As described herein, the big data that is analyzed to determineinsights is dynamic as opposed to static. Since the big data may changeover time, it follows that the determined insights may also change overtime as a result of the changes to the big data. As a result, somethingthat is determined as a best practice today may be superseded by adifferent best practice determined from the big data in the future.

In embodiments, the system is configured to continuously obtain new datain the set of big data, to continuously analyze the big data todetermine new insights, and to determine whether any of the new insightsapply to the user intent that was the basis of a previousrecommendation. In embodiments, the system makes a new (e.g., revised)autonomous recommendation to a user in response to determining that anew insight applies to a user intent that was the basis of a previousrecommendation.

To illustrate this functionality, consider the aforementioned example inwhich the user is manually creating source code for an app containingten horizontal rows on each of three pages, where each row has the sametype of widgets (e.g., an image widget, a ratings widget, and a productdescription widget). In this example, the system determined the user'sintent is creating a scrollable form that implements product leveldetail. In this example, the system autonomously recommended using aSegmentedUI instead of manually creating the repetitive components ofthe scrollable form, e.g., based on a combination of the determinedintent and the insights determined from the big data that existed at thetime the recommendation was made. Continuing this example, consider thesituation where the user accepts the recommendation by implementing aSegmentedUI in the app, and then completes and publishes the app.Sometime later, i.e., weeks or months after the app has been published,the system may determine a new insight that a different widget is thenew best practice for achieving the user's intent. Based on thisdetermined new insight, the system may make a new recommendation to theuser to revise the app using the different widget.

The new recommendation may be presented to the user in a number of ways.For example, in one embodiment the system automatically presents the newrecommendation to the user the next time the user logs in to the IDEafter the determination of the new recommendation. In anotherembodiment, the system presents the new recommendation to the user bypushing an alert to the user via a communication channel outside theIDE, such as sending an email or a text message to the user. In thismanner, implementations of the invention are configured to autonomouslymake revised recommendations to a user even after the user's app iscompleted, such that the user may choose to modify their app based onthe new recommendation.

Other Autonomous Recommendations

In accordance with aspects of the present disclosure, the autonomousadvisor functionality is configured to provide a “style advisor”recommendation in the IDE based on best practices learned from otherapps. In embodiments, the system is configured to compare style aspectsof a user's app in the IDE to best practices of these same style aspectslearned from other apps (e.g., determined from analyzing the big data asdescribed herein). The style aspects may include, for example, the size,placement, and intended use of elements within the app. For example, thesystem may determine from analyzing the event stream that an elementdisplayed in the user app is intended to be reachable by a thumb of anend user using the app on their digital device. In this example, thesystem may compare the location of the element in this app to thelocation of similar elements in other apps, and determine based on thiscomparison that the element in this app should be moved to anotherlocation in order to more easily accessible by the thumb of the enduser. The recommendation engine 2090 may generate this recommendationand the bot user interface 2010 may present the recommendation to theuser. If the user provides user input to accept the recommendation, thenthe system may provide input to the IDE to change the user's app inaccordance with the recommendation.

In accordance with aspects of the present disclosure, the autonomousadvisor functionality is configured to provide a “style evaluator”recommendation in the IDE based on best practices learned from otherapps. In embodiments, the system is configured to use patternrecognition to determine that a form included in a user app is similarto forms included in plural apps published in the marketplace. Thesystem may obtain and analyze data about these plural apps in order todetermine a respective efficiency score for each of the plural apps. Theefficiency score may be determined based on cognitive analysis of dataassociated with each of the plural apps. As an illustrative example,such data might include: ratings data (e.g., number of stars out offive) of each of the apps from the marketplace; review data (e.g., usercomments in text form) of each of the apps from the marketplace; socialmedia posts that tag, like, dislike, thumbs-up, thumbs-down, or plus-onean image and/or a textual description of the form included one of theplural apps; articles, blogs, forum discussions that include an imageand/or a textual description of the form included one of the pluralapps; and metadata that defines a level of efficiency.

Based on analyzing this data (or similar data) in the manner describedherein (e.g., using cognitive analysis and/or AI techniques), the systemmay determine an efficiency score for each form in the respective pluralapps. In accordance with aspects of the present disclosure, the systemdetermines an efficiency score of the form in the user's app bycomparing aspects of the form in the user's app to the same or similaraspects in the forms of the plural apps, and by assigning scoreweightings based on the efficiency scores of the forms of the pluralapps. For example, if an aspect of a form of the user's app is the sameas an aspect of a form of one of the plural apps that has a highefficiency score, then this determination will increase the efficiencyscore of the user app. On the other hand, if an aspect of a form of theuser's app is the same as an aspect of a form of one of the plural appsthat has a low efficiency score, then this determination will decreasethe efficiency score of the user app. In embodiments, the efficiencyscore of the user app, e.g., determined in this manner, may be visuallydisplayed in the IDE (e.g., in the bot user interface 2010) as a needleor other gauge that indicates determined efficiency score.

In accordance with aspects of the present disclosure, the autonomousadvisor functionality is configured to provide a “padding manager”recommendation in the IDE based on best practices learned from otherapps. In embodiments, the system is configured to compare paddingaspects of a user's app in the IDE to best practices of these samepadding aspects learned from other apps (e.g., determined from analyzingthe big data as described herein). The padding aspects may include, forexample, the amount of space between objects displayed on a screen of adigital device running the app, the alignment of edges of objectsdisplayed on a screen of a digital device running the app. Inembodiments, the system is configured to determine a recommended changein one or more padding aspects of the user's app based on the comparingthe padding aspects of a user's app project the best practices learnedfrom other apps. The recommendation engine 2090 may generate arecommendation including the recommended change, and the bot userinterface 2010 may present the recommendation to the user. If the userprovides user input to accept the recommendation, then the system mayprovide input to the IDE to change the user's app in accordance with therecommended change.

In accordance with aspects of the present disclosure, the autonomousadvisor functionality is configured to provide an “image advisor”recommendation in the IDE based on best practices learned from otherapps. In embodiments, the system is configured to determine a categoryof an image in user's app in the IDE, and to compare the user's image tobest practices of the determined category of images learned from otherapps (e.g., determined from analyzing the big data as described herein).In embodiments, the system is configured to determine a recommendedchange to the user's image based on the comparing the user's image toimages included in other apps that are in the same category as theuser's image, wherein the images included in other apps are selected forcomparison based on insights determined from the big data. Therecommendation engine 2090 may generate a recommendation including therecommended change, and the bot user interface 2010 may present therecommendation to the user. If the user provides user input to acceptthe recommendation, then the system may provide input to the IDE tochange the user's app in accordance with the recommended change.

In accordance with aspects of the present disclosure, the autonomousadvisor functionality is configured to provide a “design time tester”recommendation in the IDE based on best practices learned from otherapps. In embodiments, the system is configured to test the flow andexecution of a user's app in the IDE, and to compare the flow andexecution of the user's app to flow and execution of other apps. Theflow and execution may include a quantitative measure of how an appflows from one field to another field, or between forms in the app. Inembodiments, the system is configured to determine a recommended changeto the user's app based on comparing the flow and execution of theuser's app to the flow and execution of other apps, wherein the otherapps are selected for comparison based on insights determined from thebig data. The recommendation engine 2090 may generate a recommendationincluding the recommended change, and the bot user interface 2010 maypresent the recommendation to the user. If the user provides user inputto accept the recommendation, then the system may provide input to theIDE to change the user's app in accordance with the recommended change.

In accordance with aspects of the present disclosure, the autonomousadvisor functionality is configured to provide a “defect tracking”recommendation in the IDE based on best practices learned from otherapps. In embodiments, the system is configured to run the user's app inthe background of the IDE and determine the likelihood that there is adefect with a field included in the app by comparing the execution ofthe user's app to the execution of other apps. For example, a mismatcheddefinition from the back end to the front end can cause a defect, andthe system may be configured to detect this defect by comparing theruntime parameters of the user's app to the runtime parameters of otherapps, wherein the other apps are selected for comparison based oninsights determined from the big data. The recommendation engine 2090may generate a recommendation including a recommended change to theuser's app to fix a defect detected in this manner, and the bot userinterface 2010 may present the recommendation to the user. If the userprovides user input to accept the recommendation, then the system mayprovide input to the IDE to change the user's app in accordance with therecommended change.

In accordance with aspects of the present disclosure, the autonomousadvisor functionality is configured to provide a “workflow assistant”recommendation in the IDE based on best practices learned from otherapps. In embodiments, the system is configured to determine areferential integrity of a user's app in the IDE, and to compare thereferential integrity of the user's app to the referential integrity ofother apps. As used herein, referential integrity refers to a flow andefficiency of moving from one screen to another within an app. Thereferential integrity may be measured using qualitative means,quantitative means, or both. In embodiments, the system is configured todetermine a recommended change to the user's app based on comparing thereferential integrity of the user's app to the referential integrity ofother apps, wherein the other apps are selected for comparison based oninsights determined from the big data. The recommendation engine 2090may generate a recommendation including the recommended change, and thebot user interface 2010 may present the recommendation to the user. Ifthe user provides user input to accept the recommendation, then thesystem may provide input to the IDE to change the user's app inaccordance with the recommended change.

In accordance with aspects of the present disclosure, the autonomousadvisor functionality is configured to provide a “standards adherence”recommendation in the IDE based on best practices learned from otherapps. In embodiments, the system is configured to analyze a user's appin the IDE for adherence to a standard by comparing the source code ofthe user's app to data that defines a standard. The standard may bepredefined by stored data. Additionally or alternatively, the standardmay be determined based on insights determined from big data as descriedherein. For example, the system may use big data analytics as describedherein to determine that a particular existing app is highly regarded asadhering to a particular standard. As another example, the system mayanalyze the code of plural published apps to determine insights (e.g.,patterns, correlations, trends, and preferences) related to standards.The system may then compare the code of the user's app to the code ofthe particular existing app to determine where the user's app does notcomply with the standard. The recommendation engine 2090 may generate arecommendation including a recommended change to the user's app to bringthe user's app into compliance with the standard, and the bot userinterface 2010 may present the recommendation to the user. If the userprovides user input to accept the recommendation, then the system mayprovide input to the IDE to change the user's app in accordance with therecommended change.

In accordance with aspects of the present disclosure, the autonomousadvisor functionality is configured to provide a “DevOps Advisor”recommendation in the IDE based on insights determined from the bigdata. In embodiments, the DevOps Advisor recommendation includes arecommendation of one or more tools for the user to utilize in creatingtheir app in the IDE. Numerous software tools may be used in conjunctionwith the IDE when creating an app in the IDE. Such tools can becategorized as either supported or non-supported. Supported tools arethose tools that are supported by the IDE, whereas non-supported toolsare those that are not supported by the IDE but nevertheless can be usedto perform functions that are utilized in creating an app in the IDE.Examples of functions provided by such tools include: editors,debuggers, formatting beautifiers, dependency managers, update monitors,etc.

In embodiments, the system identifies supported tools and non-supportedtools and their respective functions by analyzing the big data asdescribed herein (e.g., using cognitive analysis and/or AI). Forexample, the system may use cognitive analysis to analyze data fromwebsites, blogs, forums, published articles, comments, etc., todetermine the functions for which different tools are being used byother users when creating apps. As another example, the system mayanalyze operating system data on a computer of another user to identifya tool that was used by the other user concurrently with the other userworking in the IDE, and the system may analyze the app created by theother user in the IDE to determine a function for which the tool wasused in creating the app. The operating system data may include, forexample: process execution trees; thread trees; CPU usage; and memoryusage.

In embodiments, the system also determines one or more intents of a usercreating an app in the IDE, e.g., in the manner already describedherein. According to aspects of the present disclosure, the systemcompares the user intent to the functions of the identified tools and,based on this comparison, makes a recommendation to the user to use aparticular tool to achieve the user intent. The recommendation engine2090 may generate a recommendation including the recommended tool andfunction, and the bot user interface 2010 may present the recommendationto the user. The recommendation may include information on how to obtainthe recommended tool, e.g., by indicating menu commands to access therecommended tool in the IDE; by suggesting upgrading a subscription tothe platform to obtain access to a subscription tier that includes therecommended tool; or by providing a link (e.g., a hyperlink) to awebsite where the recommended tool is available.

In accordance with additional aspects of the present disclosure, theautonomous advisor functionality is configured to provide a“refactoring” recommendation in the IDE based on best practices learnedfrom other apps. In embodiments, the system is configured to determine acomplexity of the source code of a user's app in the IDE by comparingthe source code to standards and/or best practices learned from otherapps. The standards may be predefined by stored data. The best practicesmay be determined based on insights determined from big data as descriedherein. For example, the system may use big data analytics as describedherein to determine that a particular existing app is highly regardedhaving an optimal level of complexity. As another example, the systemmay analyze the code of plural published apps to determine insights(e.g., pat patterns, correlations, trends, and preferences) related tocomplexity. The system may then compare the code of the user's app tothe predefined standards and/or the code of the existing apps todetermine where the code of the user's app is too complex. Therecommendation engine 2090 may generate a recommendation including arecommended change refactor the code of the user's app to achieve thesame functionality with less complexity. The bot user interface 2010 maypresent the recommendation to the user. If the user provides user inputto accept the recommendation, then the system may provide input to theIDE to change the user's app in accordance with the recommended change.Examples of refactoring that may be recommended include replacingmanually created source code with pre-defined widgets and/or predefinedfunction calls.

In accordance with additional aspects of the present disclosure, theautonomous advisor functionality is configured to provide a“performance” recommendation in the IDE based on best practices learnedfrom other apps. In embodiments, the system is configured to determinethe performance of a user's app in the IDE, and to compare theperformance of the user's app to performance of other apps. As usedherein, performance refers to how the app is initializing and executing,and may include quantitative measures of how the app transitions fromone screen to another, the latency of operations, etc. In embodiments,the system is configured to determine a recommended change to the user'sapp based on comparing the performance of the user's app to theperformance of other apps, wherein the other apps are selected forcomparison based on insights determined from the big data. Therecommendation engine 2090 may generate a recommendation including therecommended change, and the bot user interface 2010 may present therecommendation to the user. If the user provides user input to acceptthe recommendation, then the system may provide input to the IDE tochange the user's app in accordance with the recommended change.

In accordance with additional aspects of the present disclosure, theautonomous advisor functionality is configured to provide a “codecleanup” recommendation in the IDE based on best practices learned fromother apps. In embodiments, the system is configured to continuouslymonitor a state of the code of a user's app in the IDE, and toautonomously recommend code cleanup processes based on the monitoring.Code cleanup as used herein refers to fixing code that generateswarnings and fixing stylistic errors (e.g., beautify the layout and/orformatting). Code cleanup may include linting. In embodiments, thesystem is configured to recommend a code cleanup of an app in the IDEbased on comparing the code of the user's app to the code of other apps,wherein the other apps are selected for comparison based on insightsdetermined from the big data. The recommendation engine 2090 maygenerate a recommendation including the recommended action (e.g., codecleanup), and the bot user interface 2010 may present the recommendationto the user. If the user provides user input to accept therecommendation, then the system may provide input to the IDE toautomatically begin the code cleanup process.

In accordance with additional aspects of the present disclosure, theautonomous advisor functionality is configured to provide a “featurerequest advisor” recommendation in the IDE based on analyzing the usageof users in the IDE. In embodiments, the system is configured toidentify and analyze dead-ended processes and unfinished projects in theIDE, and to identify and recommend features to add to the IDE based onthis analysis. In embodiments, the system analyzes data logs in the IDE,i.e., those data logs associated with dead-ended processes andunfinished projects of many users using the IDE. In embodiments, thesystem also analyzes big data such as user comments about theirdead-ended processes and unfinished projects, client feature requests,etc. Based on this analysis, the system identifies functionalities thatusers want in the IDE but that are not currently available in the IDE.In embodiments, the system is configured to provide a recommendation toplatform curators to add the identified functionalities.

In accordance with additional aspects of the present disclosure, theautonomous advisor functionality is configured to provide a “newsletter”recommendation to a user of the IDE based on the user's determinedpersona and insights determined from big data as described herein. Inembodiments, the system categorizes determined best practices accordingto the set of personas (e.g., designer, developer, business owner,sales, marketing, platform, etc.). Any desired rules may be used toimplement such categorization. In embodiments, the system periodically(e.g., monthly) pushes a newsletter to the users associated with eachpersona, the newsletter for each respective persona identifying newlydetermined best practices associated with that respective persona.

Conversational Bot App Developing

The architecture 2000 may include a conversational bot app designingfunctionality that is configured to create an app in the IDE based onuser voice commands In embodiments, the conversational bot app designingfunctionality is configured to: receive voice commands from a user(e.g., via a user device); determine an intent of the user based onanalyzing the voice commands; perform actions in creating an app in abackground instance of the IDE based on a combination of the determineduser intent and determined insights (e.g., from big data); present theresults of the actions to the user device for review by the user;receive feedback from the user (via the user device) regarding thepresented results; determine an intent of the user based on analyzingthe feedback; perform additional actions in revising the app in thebackground instance of the IDE based on a combination of the determineduser intent and determined insights; and present the results of theactions to the user device for review by the user. The process mayiterate in this manner any number of times until the user is satisfiedwith the app that is created by the system based on the user input atthe user device. In this manner, implementations of the inventionprovide a system and method for user to create apps via voice commandusing their user device (e.g., smartphone) without requiring the user toprovide input via a graphical user interface (GUI) of the IDE.

FIG. 4G shows an illustrative environment in accordance with aspects ofthe present disclosure. The environment includes the architecture 2000of FIG. 2A in communication with a user device 4055 via a network 4060.The network 4060 may comprise any combination of communications networksincluding one or more of a LAN, WAN, and the Internet. For example, thenetwork 4060 may comprise the cloud computing environment 1100 of FIG.1B, and the architecture 2000 and user device 4055 may be cloudcomputing nodes 1110 as described with respect to FIG. 1B.

The user device 4055 may be a general purpose computer device such as asmartphone, tablet computer, laptop computer, desktop computer, etc. Inembodiments, the user device 4055 has a client 4065 installed thereon,the client 4065 being a program module that is configured to access theservices provided by the architecture 2000. In embodiments, the IDE isnot run on the user device 4055, and instead is run as Software as aService (SaaS) in the architecture 2000.

In embodiments, the architecture 2000 includes a conversational botmodule 4070 that is configured as an interface between the client 4065of the user device 4055 and the other elements of the architecture 2000.In embodiments, the conversational bot module 4070 comprises one or moreprogram modules in the program control 1044 of the computing device 1014as described with respect to FIG. 1A. In embodiments, the bot module4017 can be representative of the bot user interface 2010 under controlof the bot manager 3565. In alternative or additional embodiments, theconversational bot module can be representative of any of the registeredbots including, e.g., autonomous bot 3520, third-party bot(s) 3530,market place bot 3540 and/or help bot 3550.

FIG. 4H shows a flowchart of an exemplary method for conversational botapp developing in accordance with aspects of the present disclosure. Thesteps of the method of FIG. 4H may be carried out in the environment ofFIG. 4G and are described with reference to the elements depicted inFIG. 4G. At step 4080, the user device 4055 receives spoken input fromthe user, e.g., via a microphone connected to or integrated with theuser device 4055. The spoken input may comprise, for example, a verbaldescription of aspects of an app that the user wishes to create in theplatform associated with the architecture 2000. For example, the usercan verbally describe aspects, components, and functionality of the app,such as “start with a blue splash screen with the enterprise logo andapp name, then transition to a login screen with a login object for auser to enter their credentials, then transition to a home screen with amenu containing” The client 4065 transmits the spoken input to theconversational bot module 4070 via the network 4060. For example, theclient 4065 may transmit audio data of the spoken input. Alternatively,the client 4065 may convert the audio data of the spoken input to textdata using speech to text techniques, and then transmit the text data tothe conversational bot module 4070.

At step 4082, the system determines an intent of the user based on thespoken input. In one embodiment, the conversational bot module 4070converts the spoken input to a format that is usable by the otherelements of the architecture 2000 to determine an intent of the user.For example, the conversational bot module 4070 may use NLP to convertthe spoken input to data that represents events that are useable by theevent analytics engine 2070 and the event AI/NLP engine 2080 (includedin the architecture 2000) to determine an intent of the user in themanner described herein. In another embodiment, the conversational botmodule 4070 is configured to perform cognitive analysis and/or AIprocesses to determine a user intent in a manner similar to thatdescribed with respect to the event analytics engine 2070 and the eventAI/NLP engine 2080, but without passing the spoken input data to theevent analytics engine 2070 and the event AI/NLP engine 2080.

At step 4084, the system automatically performs actions in the IDE basedon the determined user intent and insights. In embodiments, the userintent that is determined as a result of the spoken input is provided tothe recommendation engine 2090 (included in the architecture 2000),which determines actions to take in the IDE (running in the architecture2000) to achieve the user intent. In embodiments, and as describedherein, the actions determined by the recommendation engine 2090 arebased on insights determined from big data. In this manner, the systemdetermines the user's intent from the spoke input, and determinesactions to take in the IDE to achieve the intent, wherein the actionsare determined based on insights (e.g., patterns, correlations, trends,and preferences) derived from analyzing the ways that others are usingthe IDE.

Still referring to step 4084, in accordance with aspects of the presentdisclosure, the recommendation engine 2090 causes the determined actionsto be performed in the IDE, e.g., via the control loop to the visualizerenhanced event engine 2040 and IDE 2030 as depicted in the architecture2000 shown in FIG. 2A. For example, the recommendation engine 2090 maycause the IDE to create screens, objects, widgets, etc., in an appproject in the IDE, wherein the screens, objects, widgets, etc., aredetermined based on the user's intent determined from the spoken inputand the insights determined from big data.

According to aspects of the present disclosure, the conversational botmodule 4070 is configured to provide data to the client 4065 to presentthe user with a visual representation of actions that were performed inthe IDE based on the user's spoken input. For example, as depicted atsteps 4086 and 4088, the conversational bot module 4070 may generate andsend to the user device screen shots of what the app looks like whenrunning. As another example, as depicted at step 4090, theconversational bot module 4070 may build the app and send a beta versionof the app to the user device 4055 so that the user can install and runthe beta version of the app on the user device 4055 for testing the appon the user device 4055.

In embodiments, the conversational bot module 4070 is configured toreceive feedback from the user via the user device 4055 and to modify orrevise the app in the IDE (running in the architecture 2000) based onthe feedback. For example, after either of steps 4088 and 4090, theprocess may return to step 4080 where the user may provide additionalspoken input to the user device 4055 after reviewing aspects of the appon the user device 4055. The client 4065 may transmit this additionalspoken input to the conversational bot module 4070, which may determineuser intent from the additional spoken input in the manner describedherein. Based on the user intent determined from the additional spokeninput, the system may determine and perform additional actions in theIDE, i.e., to revise the app in the IDE based on the additional spokeninput. In this manner, the system provides an iterative process by whichthe system receives user spoken input, determines intent of the spokeninput, performs actions in creating the app in the IDE based on thedetermined intent, and presents the results of the actions to the userfor review. This iterative process can continue until the user issatisfied with the app, at which point the system can automaticallypublish the app to the marketplace on behalf of the user, e.g., asindicated at step 4092.

Systems and Methods of Converting Actions Based on Determined Personas(Users)

As described in the previous section, in one embodiment, the system isconfigured to determine a persona of a user that is working in the UI ofthe IDE. In accordance with aspects of the present disclosure, thesystem monitors all actions performed by each user in the IDE and storesand aggregates the actions of all users according to category ofpersona. For example, when the system determines that a user has thepersona of designer, the system saves a record of all this user'sactions in a record of designer actions. The system does the same forall other users that are determined as designers, so that the record ofdesigner actions is a stored record of all actions performed by alldesigners in the IDE (e.g., an aggregate record of all the designeractions). In embodiments, the system does the same for each category ofpersona, such that there is a record of developer actions of all actionsperformed by all developer personas in the IDE (e.g., an aggregaterecord of all the developer actions), a record of sales actions of allactions performed by all sales personas in the IDE (e.g., an aggregaterecord of all the sales actions), etc.

It is often the case with an app project that different users havingdifferent personas work on the same app project at different times. Itis often the case that the different users having different personasperform different actions in the IDE when they are working on the appproject. For example, a designer may start the app project usingdesigner actions such as action editor actions, which are no-codeactions that define how an asset moves on a screen in the app. Thedesigner actions are used in the IDE for demonstrating how assets willbehave in the app; however, the designer actions are not usable in theend-product app (e.g., the designer actions are not executable in adeployed app). For this reason, the designer may then hand the appproject off to a developer who performs developer actions such aswriting code to achieve desired aspects (e.g., functions) that aredefined by the designer actions. For example, the developer uses thedesigner actions as a guide for the work that the developer performswith developer actions, i.e., coding. For example, the developer mayobserve the app functions defined by the designer actions and the writecode to implement these functions in the app.

In accordance with aspects of the present disclosure, the systemdetermines when the app project is passed from a first user having afirst persona to a second user having a second persona, and based onthis determination the system automatically converts actions that arespecific to the first persona to actions that are specific to the secondpersona. For example, the system may determine that the app project hasbeen passed from a designer to the developer. Based on thisdetermination, the system may automatically convert the designer actionsin the app project to developer actions. For example, the system mayconvert designer actions to developer actions (e.g., code such asJavaScript code) that achieve the functions defined by the designeractions. In this manner, the system automatically converts the actionsfor the subsequent user.

The conversion of actions from those of a first persona to those of asecond persona may be performed using predefined rules and storedassociations between the two different types of actions. For example,Kony Visualizer® by Kony, Inc. has a menu function that operates toconvert designer actions to developer actions. Similar functions can beprogrammed for converting actions between any two personas, such as fromdesigner to sales, from developer to sales, from developer to designer,etc. Aspects of the present invention perform the conversionautomatically in response to determining that a designer user has passedthe project to a developer user. In this manner, the systemadvantageously converts the actions for the subsequent user withoutrequiring the user to navigate menu functions to do so.

FIG. 5A shows an exemplary computing environment in accordance withaspects of the present disclosure. The environment includes thearchitecture 2000 of FIG. 2A in communication with user devices 5005 aand 5005 b via a network 5010. The network 5010 may comprise anycombination of communications networks including one or more of a LAN,WAN, and the Internet. For example, the network 5010 may comprise thecloud computing environment 1100 of FIG. 1B, and the architecture 2000and user devices 5005 a-b may be cloud computing nodes 1110 as describedwith respect to FIG. 1B.

The user device 5005 a-b may be a general purpose computer device suchas a smartphone, tablet computer, laptop computer, desktop computer,etc. In embodiments, each of the user devices 5005 a-b has a client 5015installed thereon, the client 5015 being a program module that isconfigured to access the services provided by the architecture 2000. Inembodiments, the client 5015 permits the user devices 5005 a-b to loginto the IDE that is run as Software as a Service (SaaS) in thearchitecture 2000.

In this example, the user device 5005 a is used by a first user who is adesigner that works on an app project via the UI of the IDE (e.g., IDE2030 of the architecture 2000 as described with respect to FIG. 2A). Inembodiments, and as described in the previous section titled AUTONOMOUSADVISOR, the system (e.g., the architecture 2000) determines the firstuser's persona either from an indication by the user of by analyzing theuser's actions in the IDE.

Continuing the example of FIG. 5A, the user device 5005 b is used by asecond user who is a developer that works on the same app project as thefirst user, but at a time different than that of the first user. Forexample, the first user may perform designer actions in the app projectand then save and close the project in the IDE. After this, the seconduser may open the same app project in the IDE and begin performing theirrespective developer actions in the app project.

In accordance with aspects of the present disclosure, based ondetermining that the first user has a first persona and that the seconduser has a second persona different than the first persona, the systemautomatically converts actions associated with the first persona toactions associated with the second persona. In embodiments, the systempresents the second user with a recommendation via the bot userinterface 2010 of FIG. 2A and provides the second user with an option toaccept the recommendation.

For example, as shown in FIG. 5B, the system may cause the UI 5020 ofthe IDE displayed at the second user device 5005 b to present therecommendation 5025 in the form of a chat window. The chat window mayinclude user input fields (e.g., ‘Yes’ and ‘No’ buttons in this example)that permit the second user to accept or decline the recommendation. Inthis manner, the system automatically determines that a user with adifferent persona is working with an app project and, based on thisdetermination, automatically makes a recommendation to the user toconvert the actions of a previous user to actions that are associatedwith the persona of the second user.

FIG. 5C shows a flow chart of steps of an exemplary method in accordancewith aspects of the present disclosure. At step 5030, the systemdetermines a first persona of a first user working on an app project inthe IDE. In embodiments, and as described in the previous section titledAUTONOMOUS ADVISOR, the system (e.g., the architecture 2000) determinesthe first user's persona either from an indication by the user of byanalyzing the user's actions in the IDE.

At step 5032, the system determines a second persona of a second userworking on the same app project in the IDE (i.e., the same app projectas at step 5030). In embodiments, and as described in the previoussection titled AUTONOMOUS ADVISOR, the system (e.g., the architecture2000) determines the second user's persona either from an indication bythe user of by analyzing the user's actions in the IDE.

At step 5034, the system automatically converts actions associated firstpersona to actions associated with second persona. For example, asdescribed herein, the system may automatically convert designer actions(e.g., action editor actions) performed by the first user to developeractions (e.g., coding such as JavaScript coding) for user by the seconduser.

Step 5034 may include presenting the recommendation to the second uservia the bot user interface. For example, as shown in FIG. 5B, the systemmay present a recommendation 5025 to the second user in a chat window ofthe UI 5020. The recommendation may include an explanation of theproposed conversion. The recommendation may include user input fields(e.g., ‘Yes’ and ‘No’ buttons in this example) that permit the seconduser to accept or decline the recommendation. In response to the seconduser providing user input to accept the recommendation (e.g., selectingthe ‘Yes’ button in this example), the system automatically converts theactions associated with the first persona to actions associated with thesecond persona, after which the second user may continue working on theapp project in the UI of the IDE. As an example of a conversion, thesystem may automatically generate code in the IDE that corresponds to anobject that was defined using action editor actions. In this manner, thesystem may convert the first action (e.g., the object defined usingaction editor actions) to a second action (e.g., code in the IDE thatdefines a functional instance of the object defined using action editoractions).

FIG. 5D shows a swim lane diagram of an exemplary method in accordancewith aspects of the present disclosure. In accordance with aspects ofthe present disclosure, and as described in this section, there is asystem and method to capture and aggregate multi-role user actions(designers, developers or other types) performed within a platform intoa data repository and performing analytics capable of determining futureenhancements needed to that platform.

As described herein, a same IDE is commonly used by different personassuch as UI designers, Developers, Sales and Marketing Teams, Supportteams, Product managers, etc. For example, designers mostly performactions related to the IDE canvas, developers mostly perform actionsrelated to source code modules, and sales personas, marketing personas,and product manager personas commonly use functional preview actions inthe IDE. In accordance with aspects of the present disclosure, thesystem may monitor a user's actions in the IDE and determine a personaof the user based on typical patterns of that persona, e.g., byanalyzing the user's actions in the IDE as described herein. Inembodiments, the system automatically adjusts the UI based on thecharacteristics the user is demonstrating.

With continued reference to FIG. 5D, the recommendation engine registersitself as a subscriber to all the events raised by various sub-systemsof the IDE. Based on this, the recommendation engine receives varioususer actions information in a structured format, e.g., in the mannerdescribed with respect to FIG. 4C. In embodiments, the system monitorsall user actions and preserves a record of the actions in a database(DB) repository. In embodiments, the system analyzes all the actionsperformed by users of different personas and adjust the UI of the IDEdepending on the user's persona. In embodiments, based on the analyzingthe actions performed by users of different personas, the systemdetermines and sends recommendations for platform enhancements ideas toplatform curators.

In the example shown in FIG. 5D, at step 5041, the system (e.g., thearchitecture 2000) receives an action performed in the UI of the IDE bya first user having a determined first persona (e.g., a designer). Atstep 5042, the system stores data defining the action and the persona(of step 5041) in the DB repository.

At step 5043, the system receives an action performed in the UI of theIDE by a second user having a determined second persona (e.g., adeveloper). At step 5044, the system stores data defining the action andthe persona (of step 5043) in the DB repository.

At step 5045, the system receives an action performed in the UI of theIDE by a third user having a determined third persona (e.g., sales). Atstep 5046, the system stores data defining the action and the persona(of step 5045) in the DB repository.

In embodiments, the system performs this pair of steps (e.g., 5041/5042)repeatedly and continuously for plural users interacting with the IDE.For example, plural users around the globe may be simultaneously usingthe IDE for different projects, and the system performs this pair ofsteps for each action of each user. Moreover, in embodiments, the systemaggregates actions of users of the same persona. For example, the systemstores actions performed by all designers in a designer record, actionsperformed by all designers in a developer record, and actions performedby all sales users in a sales record.

At step 5047, the system reads information from the DB repository. Inembodiments, the system reads the data for all actions of one or more ofthe defined personas. For example, the system may read the data definingall the actions performed by all users determined to be a developer.

At step 5048, the system presents an appropriate UI in the IDE of aparticular user, based on the determined persona of the user and basedon the data read at step 5047. In this manner, the UI may be customizedbased on the persona of the user using the UI. The customization mayinclude, for example, converting actions of first persona to actions ofa second persona.

At step 5049, the system performs analytics on the data obtained at step5047, and determines and sends recommendations for changes to theplatform. For example, the system may be configured to analyze the datafrom step 5047 and determine features (e.g., functionality) to add tothe IDE based on this analysis. The recommendations may be transmittedto users who are responsible for platform issues.

System and Method of Generating Actionable Intelligence Based onPlatform and Community Originated Data

As described herein, aspects of the present disclosure involve analyzingthe actions of individual platform users and community data, andperforming automated functions based on insights determined from theanalyzing. For example, the system may collect and analyze big dataincluding both internal data (e.g., platform data gathered from otherusers working with instances of the IDE that are connected to the IDXDP)and external data (e.g., community data obtained using data mining, webscraping, etc., of publicly available (e.g., Internet) data and/orenterprise data from other third party applications). In accordance withaspects of the present disclosure, the system uses cognitive analysistechniques (e.g., NLP, sentiment analysis, etc.) and AI techniques(e.g., machine learning, neural networks, etc.) to analyze the big datato determine insights about how users are using the IDE and what peopleare saying about the IDE, the platform, apps made using the IDE, etc. Inaccordance with embodiments described in this section, the systemperforms automated functions based on such insights, the functionsincluding: updating support documentation associated with the platform;identifying product features; and identifying sales leads.

Updating Support Documentation

In accordance with aspects of the present disclosure, the systemmonitors and analyzes community data and provides recommendations toupdate support documentation based on the community data. This functionmay be performed, for example, using the training/support optimizationmodule 1235 described with respect to FIG. 1C. For example, inembodiments, the system monitors community data including but notlimited to social media sources (user social media posts, comments,follows, likes, dislikes, etc.) and social influence forums (e.g., usercomments at online blogs, user comments in online forums, user reviewsposted online, etc.). In aspects, the system collects and analyzes suchcommunity data to determine insights such as questions that are asked inthe community data and, when available, answers that are provided to thequestions.

For example, the system may determine, by analyzing data from one ormore forums, that plural users have a common question about performingthe same task in the IDE (e.g., IDE 2030 as described with respect toFIG. 2A). The forums may include forums that are internal to theplatform and/or third party forums that are external to the platform(e.g., Stack Overflow, AllAnswered, etc.). One or more components of thesystem, such as the event AI/NLP engine 2080, may obtain the data fromthe forum(s) using methods described herein (e.g., data mining, webscraping, etc.) and may analyze the data using methods described herein(e.g., cognitive analysis, machine learning, etc.). The determination ofa common question may be based on NLP of user posts in the forum(s) toidentify questions that have a same topic, number of views of such postsin the forum(s) by other users, etc. Based on determining that pluralusers have a common question about performing the same task in the IDE,the system may generate and present a recommendation to a user of theplatform to provide an answer to the question. For example, therecommendation engine 2090 may generate the recommendation and cause thebot user interface 2010 to present the recommendation to a user havingthe persona of a platform curator. In response to receiving therecommendation, the user may at least one of: (i) post an answer to thequestion in one or more forums, and (ii) update the supportdocumentation associated with the platform with an answer to thequestion.

Continuing the example of determining that plural users have a commonquestion about performing the same task in the IDE, the system may befurther configured to analyze the community data from the forum(s) todetermine whether a community-accepted answer exists. For example, theremay be many different threads in one or more forums that all deal withthe same question (e.g., how to create particular object in an app), andthere may be an answer to the question posted in one of the threads.Although the answer exists, it is only in a single thread and thus mightnot be seen by many users of the forum(s) unless they happen upon thatsingle thread in that particular forum. Accordingly, aspects of thepresent disclosure mine the community data not only for commonquestions, but also for answers to common questions. The mining foranswers may be performed in a similar manner as the mining for questions(e.g., using data mining, web scraping, etc., to collect data and usingcognitive analysis to analyze the collected data). When the systemdetermines that an answer to a question exists, the system may includeboth the question and the answer in the recommendation presented by thebot user interface 2010. In this manner, the user having the persona ofa platform curator may choose how to utilize the determined answer inupdating the support documentation for the platform.

In further aspects, the system is configured to automatically update atleast one of the community data and the support documentation based onan answer determined as described herein. For example, alternatively topresenting the question and the answer in the recommendation presentedby the bot user interface 2010, the system may instead automaticallyupdate the support documentation for the platform to include thequestion and answer. In this example, the platform may include anelectronic version of a user manual, and the recommendation engine 2090may automatically change data in the user manual to include the questionand answer determined from the community data. In another example, thesystem may automatically post the answer in a community forum. Forexample, the system may have a bot account registered with a forum, andthe recommendation engine 2090 may send data to the bot account thatcauses the bot account to post a reply to the question, wherein thereply includes the answer. This may be repeated for every thread inwhich the common question is identified.

FIG. 6A shows a flowchart of an exemplary method for updating supportdocumentation in accordance with aspects of the present disclosure. Atstep 6010, the system determines that plural users have a commonquestion about performing the same task in the IDE. In embodiments, asdescribed herein, this is performed by mining and analyzing communitydata, e.g., from one or more forums, to identify a common question.

At step 6015, the system determines whether a community-accepted answerexists to the question identified at step 6010. In embodiments, asdescribed herein, this is performed by mining and analyzing communitydata, e.g., from one or more forums, to determine an answer to theidentified common question.

In one embodiment, at step 6020, the system provides a recommendation toupdate the support documentation. In embodiments, as described herein,the recommendation may be provided to a user having the persona of aplatform curator. The recommendation may be provided via the bot userinterface and/or a push notification (e.g., email) The recommendationmay include the determined question and the determined answer (if ananswer was determined). The user having the persona of a platformcurator may then utilize this information to update supportdocumentation associated with the platform. For example, the user havingthe persona of a platform curator may update a user manual or frequentlyasked question (FAQ) that is available to users of the platform. Asanother example, the user having the persona of a platform curator maypost messages to forums in response to the identified questions.

In another embodiment, at step 6025, the system automatically updatessupport documentation and/or posts an answer to the community. Forexample, as described herein, the system may automatically change datain a user manual to include the question and answer determined from thecommunity data. In another example, the system may automatically postthe answer in a community forum.

Identifying Product Features

In accordance with aspects of the present disclosure, the systemmonitors and analyzes community data and provides recommendations toidentify product features based on the community data. This function maybe performed at least in part by the product evolution module 1245described with respect to FIG. 1C.

In embodiments, the system monitors community data including but notlimited to social media sources (user social media posts, comments,follows, likes, dislikes, etc.) and social influence forums (e.g., usercomments at online blogs, user comments in online forums, user reviewsposted online, etc.). In aspects, the system collects and analyzes suchcommunity data to determine insights such as user-suggested features(e.g., functionalities) for the platform.

For example, the system may determine, by analyzing data from one ormore forums, that plural users want a user-suggested feature that is notavailable in the IDE (e.g., IDE 2030 as described with respect to FIG.2A). The forums may include forums that are internal to the platformand/or third party forums that are external to the platform (e.g., StackOverflow, AllAnswered, etc.). One or more components of the system, suchas the event AI/NLP engine 2080, may obtain the data from the forum(s)using methods described herein (e.g., data mining, web scraping, etc.)and may analyze the data using methods described herein (e.g., cognitiveanalysis, machine learning, etc.). The determination of a user-suggestedfeature may be based on NLP of user posts in the forum(s), number ofviews of such posts in the forum(s) by other users, etc. For example,the system may identify posts that say something to the effect of “Iwish the IDE did function xyz” or “product abc really needs to do xyz.”The system may also analyze how many views, replies, tags, likes,dislikes, thumbs-up, thumbs-down, or plus-ones are associated with suchposts. Based on such data and analysis, the system may determine with aquantitatively determined measure of confidence that plural users want auser-suggested feature that is not available in the IDE.

Based on determining that plural users want a user-suggested featurethat is not available in the IDE, the system may generate and present arecommendation to a user of the platform. For example, therecommendation engine 2090 may generate the recommendation and cause thebot user interface 2010 to present the recommendation to a user havingthe persona of a platform curator. In response to receiving therecommendation, the user may work with the platform development team indetermining whether and how to add the user-suggested feature.

FIG. 6B shows a flowchart of an exemplary method for identifying productfeatures in accordance with aspects of the present disclosure. At step6040, the system determines that plural users want a user-suggestedfeature that is not available in the IDE. In embodiments, as describedherein, this is performed by mining and analyzing community data, e.g.,from one or more forums, to determine the user-suggested feature. Atstep 6045, the system generates a recommendation to add the usersuggested feature to the IDE. In embodiments, as described herein, thesystem presents the recommendation to a user having the persona of aplatform curator. In response to receiving the recommendation, the usermay work with the platform development team in determining whether andhow to add the user-suggested feature.

Identifying Sales Leads

In accordance with aspects of the present disclosure, the systemmonitors and analyzes platform data and/or community data and providesrecommendations to identify sales leads based on the community data.This function may be performed at least in part by the improve salesefficiency module 1240 described with respect to FIG. 1C.

It is common for a service provider, such as an owner of a platformincluding an IDE, to permit access to the IDE on a subscription basis,with SaaS and PaaS being examples. It is also common in the subscriptionmodel for a service provider to offer different subscription levels(e.g., tiers) that have different functionalities. For example, aplatinum level subscription might have full functionality in which auser may utilize every feature in the IDE, whereas a gold levelsubscription might have a reduced functionality in which the user cannotutilize every feature in the IDE.

In accordance with aspects of the present disclosure, the systemidentifies sales leads by: identifying a feature of interest that is notincluded in a user's current subscription level; determining that thefeature of interest is included in a different subscription level;and/or providing a recommendation to upgrade to the differentsubscription level. The feature of interest may be determined in anumber of ways including but not limited to: based on platform data ofan individual user; based on platform data of an enterprise; and/orbased on community data. The recommendation may be provided directly toa subscribing user or subscribing enterprise (e.g., to an entity thathas a subscription to the platform), or may be provided to a userassociated with the platform such as a user having a sales persona inthe enterprise that owns the platform.

In one embodiment of identifying sales leads, the system identifies afeature of interest that is not included in a user's currentsubscription level by: determining an intent of a user creating an appin the IDE; determining a feature that achieves this intent; anddetermining that the feature is not included in the user's currentsubscription level but is included in a different subscription level.The determining the intent of the user is performed in a manner similarto that described in the AUTONOMOUS ADVISOR section, e.g., by usingcognitive analysis and/or machine learning to analyze at least one of:the user's events in the IDE; the user's searches for components (e.g.,assets) in the platform marketplace; the user's downloads of components(e.g., assets) from the platform marketplace; the user's searches forinformation in platform resource materials; the user's comments toforums (e.g., “I wish the IDE did function xyz” or “product abc reallyneeds to do xyz”); and data logs associated with the user's dead-endedprocesses and unfinished projects in the IDE.

The determining the feature that achieves the intent is also performedin the manner described in the AUTONOMOUS ADVISOR section, e.g., byanalyzing platform data and/or community data using cognitive analysisand/or machine learning to determine what feature(s) other users areutilizing to achieve the same intent. The determining that the featureis not included in the user's current subscription level but is includedin a different subscription level is performed by accessing andanalyzing data that defines the respective subscription levels, which isreadily available from the platform.

According to aspects of the present disclosure, the system compares thedetermined user intent to the determined features that are available inother subscription levels and, based on this comparison, makes arecommendation to the subscribing user to upgrade their subscriptionlevel. The recommendation engine 2090 may generate a recommendationincluding the recommended feature and subscription level, and the botuser interface 2010 may present the recommendation to the subscribinguser. The recommendation may include information on how to obtain thefeature, e.g., by suggesting upgrading the subscription to obtain accessto the subscription level that includes the feature.

Additionally or alternatively to providing a recommendation to thesubscribing user, the recommendation engine 2090 may generate arecommendation that is provided to a user associated with the enterprisethat owns or operates the platform, e.g., a user having a sales personawith the platform. The recommendation may include data such as: accountinformation of the subscribing user with the platform (e.g., accountnumber, enterprise name, user name, current subscription level, etc.);the determined intent; the determined feature; and the subscriptionlevel that includes the determined feature. In this manner, the userhaving the sales persona with the platform may personally contact thesubscribing user to suggest upgrading the subscribing user'ssubscription level.

FIG. 6C shows a swim lane diagram of an exemplary method for identifyingsales leads in accordance with aspects of the present disclosure. Themethod may be performed in the environment of FIG. 1C, with users 6046representing user computer devices operated by subscribing users, datasources 6047 representing computer based sources of platform data andcommunity data (e.g., platform event queue, platform marketplace,platform support forums, third party forums, etc.), architecture 6048representing the architecture 2000 of FIG. 2A, and platform 6049representing other computer devices of an enterprise that owns oroperates the architecture 2000.

At step 6051, one of the users 6046 performs one of: downloads acomponent from the platform marketplace; searches for a component in theplatform marketplace; adds a comment to a post in a forum (e.g., aninternal platform forum or an external third party forum); posts aquestion in a forum; posts an answer to a question in a forum. Step 6051may be performed plural times by plural different ones of the users. Atstep 6052, the architecture 6048 tracks and stores the entire useractivity from step 6051 in a DB repository. Step 6052 is performedsubstantially continuously in response to the many user actions at step6051.

At step 6053, one of the users 6046 uses a downloaded component (e.g.,asset) in an app. At step 6054, the architecture 6048 tracks the usageof the downloaded component (e.g., asset) from step 6053. Steps 6053 and6054 may be repeated plural times for different actions of differentones of the users 6046.

At step 6055, one of the users 6046 adds a comment in a forum. At step6056, the architecture 6048 analyzes the comment from step 6055 todetermine whether there is a product suggestion in the comment. Steps6055 and 6056 may be repeated plural times for different actions ofdifferent ones of the users 6046.

At step 6057, the architecture 6048 provides a product recommendation tothe platform 6049. In embodiments, as described herein, the architecture6048 analyzes the data from at least one of steps 6052, 6054, and 6056to identify a product recommendation (e.g., a user-suggested feature).In embodiments, as described herein, the architecture 6048 provides arecommendation to a user associated with the enterprise that owns oroperates the platform, e.g., a user having the persona of a platformcurator. In response to receiving the recommendation, the user may workwith the platform development team in determining whether to add theuser-suggested feature.

At step 6058, the architecture 6048 tracks and analyzes usage ofcomponents (e.g., assets) by one or more users 6046 and suggests asubscription upgrade based on the analyzing. In embodiments, asdescribed herein, the system identifies a feature of interest that isnot included in a user's current subscription level, and identifiesanother subscription level that includes the feature. Based on this, thesystem either provides a recommendation directly to the subscribinguser, or provides a recommendation to a user associated with theenterprise that owns or operates the platform, e.g., a user having asales persona with the platform.

Interactive Tutorial Platform and Related Processes

The interactive tutorial platform and related processes comprise asystem and method for the dynamic conversion of a software component(e.g., asset) comprised of data and meta-data into a real-time playbackof component creation. This interactive tutorial platform provides thecapability to use components within a component library or marketplace,and to educate a designer, developer or other associated skills role inthe functionality, the runtime characteristics and the end to endintegration of a software component. In addition, the interactivetutorial platform implements DevOps, which is a combination of culturalphilosophies, practices, and tools that can increase an organization'sability to deliver applications and services at high velocity.Basically, the DevOps will evolve and improve products at a faster pacethan using traditional software development and infrastructuremanagement processes.

In embodiments, application developers create different types ofreusable code/UI, which are called “components”. These components arepart of different assets such as skins, widgets, platform specificicons, buttons, or other assets, etc. These components include twopersonas: 1) component producer and 2) component consumer. The componentproducer defines the component in a meta file; whereas, the componentconsumer can download the component to his/her application via anapplication development tool, for example. The consumer typically goesthrough the meta info to understand the usage of this component. This isa tedious and time consuming process, which is not very effective.

In contrast, though, the interactive tutorial platform described hereinassists the consumer to consume the component by using an interactivetechnique. More specifically, the interactive tutorial platform andrelated processes allow for components to tell a story about themselves;that is, the interactive tutorial platform described herein is capableof learning about the different components, putting them into theirconstituent components and providing an interactive tutorial to the userfor building the components in a development application environment.The interactive tutorials can be used directly in the workspace, as theuser (consumer) is building the component. In this way, the consumer isnot only learning how to build the component, but is also actuallycapable of building the component in the application developmentplatform in real-time.

With this noted, FIG. 7A shows a schematic or architectural view of theinteractive tutorial platform and related processes. In embodiments, theinteractive tutorial platform and related processes can be implementedusing the infrastructure shown in FIG. 1A, e.g., computing environment1000. The interactive tutorial platform 7000 can also be imported fromany marketplace, e.g., Kony Marketplace™ by Kony, Inc. into theworkspace of the development application, e.g., Kony Visualizer®, andsubsequently “played”. The interactive tutorial platform can also be aplug-in (e.g., plug-in architecture module 1230 shown in FIG. 1C) to anydevelopment tools or other computing infrastructure and/or any of thesubsystems described herein. In this way, a training and supportenvironment, e.g., an interactive tutorial, can be provided directly tothe user (developer) in an application development environment while theuser is building the component.

In FIG. 7A, the interactive tutorial platform 7000 includes an analysiscomponent 7010 and machine learning component 7015. In embodiments, themachine learning component 7015 can be a subcomponent of the analysiscomponent 7010 or a standalone component. In further embodiments, theanalysis component 7010 can include AI as described herein for analysisof assets and associated metadata. In further embodiments, the analysiscomponent 7010 and machine learning component 7015 can be sub-componentsof the training/support optimization module 1235 shown in FIG. 1A,configured to create and view an interactive type of tutorial fortraining a user in the construction or use of different assets. Inaddition, the analysis component 7010 and machine learning component7015 can be implemented through the help bot 3550 shown in FIG. 3D.

By way of illustrative example, the analysis component 7010 can analyzecomponent meta files and construction format of a current asset(hereinafter referred to as a component) being built by the user, aswell as components which are imported from other development tools,marketplaces, etc. By making such analysis, the analysis component 7010can determine the exact method of construction of the component. Inaddition, by using this obtained information, the analysis component7010 can transform the component into an interactive tutorial for otherusers for providing knowledge, education and training about thecomponent. This can be done by using, for example, the machine learningcomponent 7015 or, alternatively, in a standalone mode, as furtherexplained herein.

As should be understood by those of ordinary skill in the art, a metafile is a generic term for a file format that can store multiple typesof data. This commonly includes graphics file formats. These graphicsfiles can contain raster, vector, and type data, amongst otherinformation. In the examples provided herein, the meta file can alsoinclude information about the component, as written or explained by thecreator or a third party source. The analysis component 7010 can alsoanalyze DevOps of the components, making comparisons to best practices,for example.

In specific embodiments, the analysis component 7010 can create aninterpretation layer, which is capable of processing the internal fileformats of a component and replay the construction of that componentwithin an IDE. By way of illustrative example, the interpretation layercan determine and pipe each of the steps derived from the design ofdifferent components or assets such as skins, widgets, platform specificicons, buttons, etc., to visualize its appearance in the developmentapplication workspace.

In embodiments, metadata that is analyzed can include timing data,comments, special symbolic indicators such as boxes and arrowseffectively creating a dynamic technology transfer. The analyzed datacan then be used, e.g., to build a video, in the development tool (e.g.,Kony Visualizer® by Kony, Inc) by representing the componentsyntactically in JavaScript Object Notation or JSON, which is anopen-standard file format that uses human-readable text to transmit dataobjects consisting of attribute—value pairs and array data types (or anyother serializable value). As should be understood, JSON is alanguage-independent data format. This technology transfer results inthe development, e.g., rendering of the interactive tutorial, with theuser observing the interactive tutorial of the asset (component). Theuser can also import or inspect any component for inclusion into a blankor pre-populated application project, and become educated in thefunctionality of the component.

The interactive tutorial platform 7000 further includes a machinelearning component 7015. The machine learning component 7015 is capableof gathering global trends for particular assets or components in orderto include such information in the interactive tutorial. In embodiments,the global trends can be gathered by the global developer trends module1215 as shown in FIG. 1C. The machine learning component 7015 can alsogather the different assets or components from remote sources, e.g.,blogs, other development tools, marketplaces, etc., by any known datamining technique, e.g., crawlers, spiders, etc. This information canthen be provided to the analysis component 7010 or directly to theinteractive display 7020. If provided to the analysis component 7010,the analysis component 7010 will then analyze the different assets orcomponents, e.g., component meta file and construction format, for usein the interactive tutorial, which may be displayed on the interactivedisplay 7020.

The machine learning component 7015 extends to insight generated by AI,which can inject commentary of the component gathered from an extensivecollection of users who have previously commented on the component, aswell as the intergradation by the AI of the machine learning component7015. Moreover, the machine learning component 7015 and/or analysiscomponent 7010 can provide comparison and contrasting interpretationsbased on the analysis of a plethora of components. These commentaryincluding the comparisons, etc. can be wrapped an object so that acomponent can be “explained” in a particular point of view or by aparticular user. The analysis component 7010 and/or machine learningcomponent 7015, using the AI described herein, can automaticallygenerate the commentary, which can be wrapped around the object withinsightful meta-data so that a component can be “explained” in aparticular point of view.

In embodiments, the commentary can be provided by a natural languagecomponent 7030. The natural language component 7030 can create originalcommentary or commentary based on previous user comments on thatcomponent or by the analysis of the meta file, etc. As should beunderstood by those of ordinary skill in a meta file is a generic termfor a file format that can store multiple types of data. This commonlyincludes graphics file formats. These graphics files can contain raster,vector, and type data, amongst other information. A common use for thesefiles is to provide support for an operating system's computer graphics.In the examples provided herein, the meta file can also includeinformation about the component, as written or explained by the creator.

In embodiments, the natural language component 7030 can updatecommentary based on actions by specific users or developer, e.g.,modifications to the components, user comments, etc. In furtherembodiments, the systems and methods described herein will allow a useror specific users (e.g., depending on the particular settings) toprovide updated commentary. Any of these updated commentaries can beshared in subsequently viewed tutorials of the component. In addition,the tutorials (or commentary) can be updated as the component or assetis changed or updated by another user, using the components describedherein. The commentary and tutorials can be saved in the storage system1022B shown in FIG. 1A. Alternatively or in addition, the commentary andtutorials can be saved locally in any marketplace or development tool,or remotely in the cloud or other server.

Still referring to FIG. 7A, the machine learning component 7015 and/orthe analysis component 7010 can include AI to determine or identify anydifficulties the user may be encountering in the application developmentprocess. For example, the analysis component 7010 and/or machinelearning component 7015 can determine, e.g., if certain assets designedand/or written by the user will not work, if there is a “bug”, if theuser has encountered a “dead end” (e.g., the user cannot continue due tosome difficulty), etc. In these and other examples, the machine learningcomponent 7015 and/or the analysis component 7010 can find a newopportunity for the user, and provide such new opportunity to the userin an interactive format (or tutorial as described herein). This newopportunity can be global trends e.g., a different or easier way ofdoing things, or other information found by searching the Internet,blogs, social media, other development tools or digital applications,marketplaces, etc.

The interactive tutorial platform 7000 can further expose a ratingswidget 7025 in a community or marketplace, which allows for thereflection of satisfaction levels associated with user submittedcommentary. For example, the ratings widget 7025 can rate each of thecommentaries by gathering information from the users, or noting anynegative or positive feedback from the different commentaries ortutorials. The ratings widget 7025 can also rate the tutorials by howmany times it was used, whether any changes were made to the tutorial orany changes made to the commentary, as examples.

FIG. 7B shows an exemplary display with an interactive tutorial usingthe interactive tutorial platform in accordance with aspects of thepresent disclosure. As shown in FIG. 7B, the commentary 7115 can beprovided in a bubble format, a playback format or a narration which willinform the user how the component (or service) was actually created.Accordingly, the commentary 7115 can be optional narration tags whichcan specifically be rendered as balloon text or spoken words andessentially performs a dynamic technology transfer to the user.Basically, the commentary, which was generated by the systems andmethods described herein can show each step of the component's creationand can behaves similar to another developer or designer taking the userthrough the creation of the component.

More specifically, FIG. 7B shows an exemplary display 7100 comprised ofthe component 7100 being built by a developer or other user (or importedfrom another source). In embodiments, the component 7110 is a button;although, it should be understood by those of skill in the art that thecomponent 7110 can be any component being built by a user or which auser wants additional information. As further shown, the display 7100includes a commentary box 7115. In embodiments, the commentary box 7115can be optional narration tags which can specifically be rendered asballoon text or spoken words and essentially performs a dynamictechnology transfer to the user, e.g., tutorial as to how to build theparticular component.

As noted already herein, the commentary box 7115 will be auto-generatedby the interactive tutorial platform, which will provide a step by steptutorial and/or comments related to the particular component 7110. Forexample, in the case example shown in FIG. 7B, the commentary box 7115can be provided by a chat bot as described herein, and can provide acomment on how to place the arrow box in a certain location, how toenlarge or decrease its size, how to modify the shape, or otherinstructions. In addition, the commentary box 7115 can provide a step bystep analysis of other information of the component 7110 and, as thecomponent changes in the build process, the commentary box 7115 canautomatically be updated to show the steps of the change. In addition,the component 7110 can be either a static representation of thecomponent or part of a video with accompanying narration or commentaryshowing the entire process of building the component 7110.

As further shown in FIG. 7B, the display 7100 includes a menu 7120. Inembodiments, the menu 7120 provides the user with the ability to performmany different functions, as implemented by the interactive tutorialplatform. For example, the user can add or edit the comment box, build anew component (in which case the interactive tutorial platform willanalyze the meta file, etc., and provide a new tutorial), provide oredit narration, provide a rating to the current comments, etc.

FIG. 7C depicts a swim lane diagram for the interactive tutorialplatform in accordance with aspects of the present disclosure. The swimlane diagram 7200 includes three actors: the application developmenttool (e.g., Kony Visualizer® by Kony, Inc), the interactive tutorialplatform and a marketplace or other source for a component. At step7210, a user (e.g., using the application development tool) requests toadd a component from library or marketplace or other third party source.For example, the library or marketplace can be any third party source,e.g., blog, development tool, service provider, etc. At step 7220, thecomponent is downloaded from marketplace or other source and added tothe current form in the current project. At step 7225, the interactivetutorial platform reads the meta info (e.g., component meta andconstruction format) and any user added commentary associated with thedownloaded component. At step 7230, the interactive tutorial platformcreates a step-by-step interactive user help system (e.g., tutorial) andpresents it to user to the user (e.g., on the development tool). Asalready noted herein, the interactive tutorial platform can generate thestep-by-step interactive user help system using Natural LanguageGeneration (NLG) based on previous user comments on that component or,intuitively, the component meta and construction format. In this way,the interactive tutorial platform can create an interactive help systemto guide the user about the component.

System and Method of Real-Time Collaboration

The system and method for real time collaboration is provided betweendevelopers within the enterprise and a community of developers. Forexample, multiple developers and designers can work on a single project,with the developers working on subsections of the single project atdifferent locations. These developers can now interact with each otherand with the designer by sharing the project context etc.

In embodiments, the system and method for real time collaboration allowssystem administrators to define collaboration rules across theenterprise users or at the developer level. For example, the system andmethod for real time collaboration allows:

1) the developers to connect with the other developers in the enterpriseto collaborate, discuss issues etc.;

2) the developers to connect with the external world (outside of theenterprise) if they have the permission from the administrator; and/or

3) users to take screenshots, capture build logs from the IDE and toshare them with others.

In embodiments, the administrators of the system can define rules andthe level of collaboration that is allowed through the administrativeportal 8000 as shown in FIG. 8A. The administrative portal 8000 and itsconstituent components (e.g., library, etc.) can be implemented in theillustrative environment 1000 of FIG. 1A. In additional or alternativeembodiments, the administrative portal 8000 (e.g., system and method forreal time collaboration) can be a standalone system with a plug-inarchitecture (e.g., plug-in architecture module 1230 shown in FIG. 1C)to any of the components or systems described herein. For example, thesystem and method for real time collaboration can be a plug-in to amarketplace or any enterprise system providing any of the functionalitydescribed herein. In additional embodiments, the administrative portal8000 can be a subsystem of the admin console manager 3580 shown in FIG.3E.

Through the administrative portal 8000, the administrator or otherauthorized user can create, configure and/or update security policies(rules) for providing permissions to share information amongst differentusers as shown representatively at 8010, e.g., collaborating users. Inembodiments, the users, e.g., developers can register with theadministrative portal 8000, such that the administrative can appendcertain rules or policies to each of the users. In this way, theadministrative portal 8000 can act as a centralized portal for theadministrator to set policies, for the users (developers) to requestcollaboration with other users that have registered with the system, andto provide information to the requesting user about the requested user.

In embodiments, the administrator can set policies from a policy library8015 which, e.g., implements the processes described herein. The policylibrary 8015 can be a storage system as shown, e.g., in FIG. 1A or astandalone database or other storage system. In embodiments, the policylibrary 8015 may be implemented as separate dedicated processors, or asingle or several processors to provide the function of this tool. Thepolicies from the policy library 8015 can be provided to each of theregistered users 8010 of a development tool. For example, the policiesfrom the policy library 8015 can be packaged with other communications,a component, assets, or a tutorial, etc., by appending the policydirectly thereto and sending it to the user. The policy can be aneXtensible markup language (XML) file or other type of file thatcontains the policy, itself. In embodiments, the policies, in turn, canbe cached in the development tools of the users for later use.

It should be understood that that any number of policies can be providedby the administrator, depending on many factors. That is, the policiescan be targeted on a much more granular level than just the user orproject being worked upon by the different users. For example, adifferent policy can be configured taking into account the applicationID, device platform (e.g., such as iOS™, Android®, Blackberry®, WindowsMobile™, etc.), specific device ID, specific user ID, devicecharacteristics and/or the user's group information. Also, certainlevels of security or enforcement policies can be matched to a userbased on the user, e.g., level of security, types of applications, typesof digital devices, etc. These policies can include, amongst otherexamples:

1) block collaboration between users on certain device hardwareelements;

2) block collaboration between users that have a virtual private network(VPN) connection;

3) allow collaboration between users that are on certain VPN connection;

4) require collaboration between users only over a secure connectionsuch as using a secure sockets layer (SSL);

5) limit collaboration to certain IP addresses, domains and/or ports theuser is allowed to connect to (firewall for applications);

6) allow offline collaboration between users;

7) require collaboration between users that are using encryption;

8) disable or enable document sharing including, e.g., build logs, USscreenshots, working canvas and/or libraries, etc.; and/or

9) allow collaboration between users only during a specific date or timespan (e.g., business hours) or at specific locations.

The enterprise administrator can update or create these policies,independent and regardless of any specific application or user. Theseupdated policies can not only change the rules, but how and when theyare applied based on the many different factors. For example, the rulescan be changed based on company or enterprise policies, e.g., a leaddeveloper of the enterprise can collaborate with any other developer,regardless of location, time, or platform. This can be achieved bynoting the user ID and password, at startup or authentication. Inembodiments, a developer can independently develop a digitalapplication, without knowledge of the policies.

FIG. 8B depicts a swim lane diagram for implementing steps associatedwith the administrative portal to provide rules for real-timecollaboration in accordance with aspects of the present disclosure. Theswim lane diagram 8100 includes several actors: developer 1, developer 2and the system of collaboration, e.g., administrative portal 8000. Itshould be understood by those of skill in the art that more than twodevelopers can be part of this flow, with two developers being used forillustrative purposes only.

At step 8110, the developer 1 registers with the administrative portal.At step 8115, the developer 2 registers with the administrative portal.Through the administrative portal, the administrator can assign specificrules to each of the developers. It should be understood that otherdevelopers or users, e.g., designers, collaborators, etc. can alsoregister with the administrative portal. At step 8120, developer 1 willrequest a search for developer 2 at the enterprise level or from aglobal app developer community using, e.g., the administrative portal.In embodiments, the administrative portal will search for developer 2,determine its specific rules associated with developer 2 and, if accesscan be granted for collaboration, the administrative portal will sharedeveloper 2 information so that developer 1 can add developer 2 as acontact. At step 8130, depending on the specific rules with each of thedevelopers, developer 1 and developer 2 can begin interaction with oneanother. This interaction can be any type of allowed collaboration suchas sharing build logs, UI screenshots, working canvas and libraries,etc.

System and Method for Connecting End Users to Business Systems

The system and method for connecting end users to business systems is asystem and method that enhances service and generates business leads. Inembodiments, the system will allow the users with certain roles (e.g.,managers, lead developers, etc.) to request help for professionalservices, buy products, proposals, etc., through a chat interface. Inembodiments, the system and method for connecting end users to businesssystems routes these requests to customer relation management systemsand/or through the business systems and logs the requests. The systemwill also autonomously respond back to the users when the previouslysent requests are updated by the interested parties. By way of morespecific example, by implementing the systems and methods describedherein, users from any enterprise can connect with a sales team aboutnew products and upgrades. The user can also request help fromprofessional services.

Referring to FIG. 9A, a user interface 9000, e.g., chatbot, allows auser to connect with sales and professional teams on a remote computingsystems. In embodiments, the user interface 9000 can be representativeof the computing environment 1000 of FIG. 1A and be implemented with anyof the subsystems described herein. For example, the user interface 9000can be implemented and/or plugged into the improve sales efficiencymodule 1240 of FIG. 1C. Here, an IQ engine has the ability to feedmetrics into a salesforce service or a salesforce database which, inturn, is provided to a member of a sales support team informing them ofpotential upsell opportunities.

By implementing the user interface 9000, for example, a user can sendtheir proposal and/or requests to a sales and professional team. Theuser can use the user interface 9000 to request help for professionalservices, buy products, proposals through the chat interface. Inembodiments, the user can be one of many different roles. For example,the user can be a manager, a lead developer, etc. The requests will berouted to a customer relation management systems 9020 and/or through thebusiness systems 9030, depending on the communication and the requestedtype of information. In embodiments, the customer relation managementsystems 9020 is a technology for managing a company's relationships andinteractions with customers and potential customers to improve businessrelationships. When the user interface 9000 receives the response fromsales/marketing and professional teams (or customer relation managementsystems 9020 and/or through the business systems 9030), it notifies theuser with same response. This can be done by an alert system 9010. Thealert can be an email, text message, an audible alert, etc.

In embodiments, the user interface 9000 can include a plug-inarchitecture (e.g., plug-in architecture module 1230 shown in FIG. 1C),plugging into any of the sub-systems described herein. In combinationwith AI or machine learning (as described herein), the user interface9000 can monitor the sub-systems described herein and, by determiningcertain actions of the user, it can autonomously request informationfrom the customer relation management systems 9020 and/or through thebusiness systems 9030. For example, by determining that the user hastaken a certain action, the user interface 9000 can determine that newsystems may be needed to more efficiently provide such action andrequest information from the customer relation management systems 9020and/or through the business systems 9030 of such systems. Theinformation can then be provided automatically to the user, through theuser interface 9000.

The requests can be logged in the user interface 9000 and/or customerrelation management systems 9020 and/or through the business systems9030. By logging the requests, the systems and methods described herein(e.g., chatbot) can continue a query into the customer relationmanagement systems 9020 and/or through the business systems 9030, andautonomously respond back to the users when the previously sent requestsare updated or found. For example, using the AI residing on the userinterface 9000, the systems and methods described herein can parse thequestions/requests of the user and when an answer is found to such arequest, the management systems 9020 and/or through the business systems9030 can autonomously send back a response. In embodiments, the responsecan be a machine generated response or a human response. In any event,the logged information will be maintained in a queue until it isanswered, at which time it can be removed or flagged as complete.

FIG. 9B depicts a swim lane diagram for implementing steps associatedwith the communication with the business systems in accordance withaspects of the present disclosure. The swim lane diagram includes atleast three actors: developer/user, the system which connects the enduser to a business application and the business application, itself,e.g., customer relationship management (CRM). At step 9110, the userregisters with the systems described herein, e.g., chatbot. Inembodiments, the user can be a manager or lead, for example, amongstother users. At step 9115, the user requests professional servicesand/or proposals for products, features or some services from thechatbot. At step 9120, the chatbot will forward the request to the salesteam, customer relationship manager, or other internal or externalbusiness solution system, etc. At step 9125, the customer relationshipmanager, or other internal or external business solution system willrespond with solutions and/or answers. At step 9130, the chatbot willautonomously forward/respond to the user with the solutions proposed bythe management systems and/or through the business systems.

Registration System

The registration system allows vendors to register themselves with theany combination of the systems/sub-systems described herein using anadministrative portal/website (e.g., administrative portal 8000). Inembodiments, the registration system can be a plug-in architecture(e.g., plug-in architecture module 1230 shown in FIG. 1C) to any of thesubsystems or other computing infrastructures described herein. Theregistration system can also be represented by the computinginfrastructure shown in FIG. 1A.

In embodiments, the registration system is capable of digitalizing thethird party vendors communication with a third party service providersystem e.g., Kony® of Kony, Inc. Once the registration is successful,these vendors are allowed to interact with other vendors using theirunique ID. For example, as shown in the swim lane diagram of FIG. 10A,at step 10010, the system will allow third party vendors to plug inthrough the administrative portal. At step 10015, the registrationsystem will assign each vendor a unique handle. At step 10020, theregistration system will provide the unique handle to a third partyservice provided, e.g., vendor. This handle is used by the end users ofthe system to communicate with the external vendors through theregistration system, as shown at step 10025. In embodiments, theregistration system can also define a communication contract for theexternal vendors.

System and Method to Campaign Products and Services (Marketing System)

The system and method to campaign (e.g., identify and sell) products andservices has the ability to run marketing campaigns forproduct/professional services with a targeted audience. In embodiments,the system and method described herein can be implemented by thecomputing environment 1000 of FIG. 1A, which campaigns can be displayedon any of the interfaces described herein. In addition, the campaign canbe implemented through a plug-in (e.g., plug-in architecture module 1230shown in FIG. 1C) to work with any of the sub-systems described herein.In embodiments, the system and method to campaign products can beimplemented through the improve sales efficiency module 1240 describedwith respect to FIG. 1C.

The products and services can be provided in a campaign via adevelopment tool such as Kony Visualizer® by Kony, Inc. or through amarketplace or other third party service provider. Information can becollected from the users and fed back from Kony Visualizer® or otherthird party service provider. In embodiments, as with any of the systemsand related functionality described throughout the disclosure, the usercan opt-out of the information gathering mode and/or campaign. In thecase that the user does not opt-out, any information obtained from theuser can be preserved in a database, e.g., storage system 1022B shown inFIG. 1B or other central repository, for example.

In embodiments, the system and method described herein can run andinitiate marketing campaigns with selected users by taking feedback fromusers and by providing the user with a unique set of questionnaires. Thesystem and method can also provide notifications to the users, whichhave provisions for the user to turn on/off such notifications.

In implementing the system and method described herein, AI (and/ormachine learning) can run analytics from the response data received fromvarious users and provide suggestions/details to the different platformsdescribed herein. For example, the system will have the ability to runproduct/professional services marketing campaign with the targetedaudience based on the data received from various users. The system andmethod also has access to all its users via identity services, e.g.,Fabric Identity Services' of Kony, Inc. In embodiments, the identityservices are a centralized system capable of creating and transferringtokens between different systems, inserting the appropriate tokens atthe appropriate locations for the different systems.

FIG. 11A depicts a swim lane diagram for implementing steps associatedwith a marketing system in accordance with aspects of the presentdisclosure. The swim lane diagram 11100 of FIG. 11A includes threeactors: users (e.g., developers), a centralized management system (e.g.,Kony IQ™ by Kony, Inc.) and a marketing system. In one example, thecentralized management can be representative of the computingenvironment 1000 shown in FIG. 1A implemented as the improve salesefficiency module 1240 shown in FIG. 1C.

At step 11010, a developer or other user can enable or disable thenotifications and/or campaign. At step 11015, the marketing system caninitiate the notifications and/or campaign for products and/orprofessional services. At step 11020, the centralized management system(e.g., Kony IQ™ by Kony, Inc.) can provide the notifications and/orcampaign for products and/or professional services to the user. At step11025, the user can provide feedback to the centralized managementsystem (e.g., Kony IQ™ by Kony, Inc.) about the notifications and/orcampaign for products and/or professional services. At step 11030, thecentralized management system (e.g., Kony IQ™ by Kony, Inc.) can storethe results and run analytics, providing them to the marketing system.In embodiments, the analytics can be run using the AI as describedherein. The analytics can be, e.g., effectiveness of the campaign,whether it is targeting the correct users with the most pertinentproducts and/or services, etc.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed. Cloud computing is a model of service delivery forenabling convenient, on-demand network access to a shared pool ofconfigurable computing resources (e.g. networks, network bandwidth,servers, processing, memory, storage, applications, virtual machines,and services) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service. Thiscloud model may include at least five characteristics, at least threeservice models, and at least four deployment models.

System and Method of Application Translation

The system and method of application translation can be implemented inprepackaged applications, e.g., digital banking and other commercerelated applications provided to customers. The system and method ofapplication translation allows customers to customize the applicationsto meet their requirements, while still providing for translations. Forexample, the system and method of application translation can beimplemented in a prepackaged application that will now supportinternationalization (i18n) of many different languages using, e.g.,translation APIs. In embodiments, by implementing the system and methodherein, prepackaged applications will support i18n on all strings in theapplication, which, in turn, will reduce issues at runtime.

Adding a new language support to applications is very challenging. Forexample, problem areas in internationalization may include client sideissues, server side issues and other miscellaneous issues. In computing,internationalization and localization are means of adapting computersoftware to different languages, regional differences and technicalrequirements of a target locale. Internationalization is the process ofdesigning a software application so that it can be adapted to variouslanguages and regions without engineering changes. Localization is theprocess of adapting internationalized software for a specific region orlanguage by translating text and adding locale-specific components.Localization (which is potentially performed multiple times, fordifferent locales) uses the infrastructure or flexibility provided byinternationalization (which is ideally performed only once, or as anintegral part of ongoing development)

By way of illustration, client side i18n issues may include: static textset to widget and no i18n key; static text set to widget after settingi18n key; static text set to widget property directly; static text setto widget property indirectly (via another variable); text set to widgetand no i18n key onc, but text is set in code; images with English textor which represents locale or regions in it; splash screen; date andtime formats; currency symbol format; currency separator in numbers;keyboard and soft input panels; charts/custom calendars/custom widgetswith user interfaces; browser content; map content;native/pickers/choosers; network call returned text in English; staticEnglish text concatenated with dynamic text; and i18n text concatenatedwith dynamic English text. Server side i18n issues may include: textsaved in master tables in a DB; text in child tables in the DB; textconstructed with raw text in network call response; indirect text suchas URLs in master/child tables; and images in child tables with languageor region specific. Miscellaneous i18n issues may include: semiinternationalized and localized applications; not all strings areexternalized; language mappings in source code; conditional code basedon language or region; currency mapping based on language; date timeformats based on language and region; custom charts with English text;browser content with English URLs; custom widgets (calendar) with staticEnglish text; and network calls are returning English text always.

The systems and methods of the application translation solve the aboveproblems. For example, by implementing the system and method providedherein, the following can be provided during application conversion:

1. Extracting all strings from all forms, widgets, applications, etc.,and localizing the extracted strings with i18n keys;

2. Localizing master and child tables in a database having English text,e.g., in localized format;

3. Localizing strings on application developer written code whichperform string operations on strings;

4. Localizing custom widgets (e.g., calendar widget) at multiple places,advertisement image URLs, form headers, etc.

5. Allowing translate APIs to translate all strings from a set;

6. Deploying on public cloud without login failure;

7. Changing formatting in application code and databases;

8. Supporting charts, maps, etc. in different languages; and

9. Returning network calls in English text and/or other languages; and

10. Rending translated content in a browser.

In embodiments, the system and method described herein can beimplemented by the computing environment 1000 of FIG. 1A, withtranslations being displayed on any of the interfaces described herein.In addition, the system and method of translation can be implementedthrough a plug-in (e.g., plug-in architecture module 1230 shown in FIG.1C) to work with any of the sub-systems described herein. Inembodiments, the system and method can be implemented through theCognitive Computing for Omni-Channel Platform described with respect toFIG. 1C.

FIG. 12A depicts a flow diagram for implementing steps associated withthe system and method of translation application. The features describedherein can be implemented from the centralized management which isrepresentative of the computing environment 1000 shown in FIG. 1A. Morespecifically, FIG. 12A shows the translation process which isimplemented by the centralized management system (e.g., Kony IQ™ byKony, Inc.).

At step 12005, all properties of widgets which show text on a userinterface are obtained, e.g., known. In embodiments, the centralizedmanagement system can know the properties of the widgets because it willintegrate with the reference architecture of all forms, widgets andapplications (which include the widgets and the forms). Morespecifically, the reference architecture (which is shown in FIG. 12B)allows for a very robust representation of widgets, forms andapplications so that the centralized management system can becomeintegrated such that it implicitly knows the format of the widgets,forms and application. In further embodiments, the centralizedmanagement system will be able to understand the formatting and syntaxof any application and, further, is capable of accepting modificationsand handling applications outside the context of the platform associatedwith the centralized management system.

At step 12010, the centralized management system identifies all the textproperties of the widgets (or applications or forms) which do not havean i18n key, from all forms, templates, masters, and widgets in thecurrent application. At step 12015, the centralized management systemgenerates new i18n keys and sets these keys to text properties whileremoving the direct text properties. For example, in this step, thecentralized management system can upgrade the application in real timeby dynamically creating additional JSON notation with respect to thatparticular field. In this implementation, the new i18n keys can besynthesized, with new format and syntax representing text propertiesthat support the i18n key for that particular field or widget etc.,which is then written into the project side. The initial text propertiescan then be removed as they have been updated and enhanced.

At step 12020, the centralized management system scans through all thesource code files using JavaScript AST. In embodiments, the JavaScriptabstract syntax tree (AST) helps collect all the widget properties thatare a part of the scope of the application in order to set the widgetproperties both directly and indirectly. For example, the JavaScript ASTessentially rifles through all code and gathers up all the strings thatare needed to reset in terms of translation capabilities. To identifywhich screen string sets are needed in order to perform the translationcapabilities, it should be understood that the screen string will have aunique identifier for a text field and if that text field has somethingpre-initialized in it, then the text field is reviewed and it is assumednecessary to create a different embodiment by translating thatparticular field.

At step 12025, the centralized management system will generate the newi18n keys and change the code with an API call, e.g.,kony.i18n.getlocalizedString( ) API calls, with the new generated i18nkeys. For example, in embodiments, the centralized management systemcalls using a particular get localized strings function and maps this toa translate program. More specifically, the centralized managementsystem would use the i18n keys to pass a source language to set a targetlanguage for translation, and then collects the translation when itreturns from that particular service call. In embodiments, by using areverse translation technique, it is also possible to translate thetarget translation back into the original source language for comparisonpurposes. This can be done by flagging any translations that it'sbelieved to be out of specification or out of compliance.

At step 12030, all of the collected strings are added to an i18n mapalong with their i18n keys. In embodiments, the i18n map can be acurrently existing map or a map, which includes the JSON notations. Forexample, the centralized management system has taken care of essentiallyrewriting all the JSON notation that represents the i18n key structure,and all the fields, and fields by language, etc. As noted below, oncethis structure is saved the particular project does not need to betranslated again.

At step 12035, a translation API (e.g., Google™ translation API) willconvert all the identified strings into the target language, e.g.,English to Mandarin, etc. At step 12040, the centralized managementsystem adds the new language and the newly translated strings to theapplication i18n map. At step 12045, the centralized management systemsets the target language to the new language.

In embodiments, the target language can be driven in many differentways. For example, the target language can be driven by a top levelinterface of the centralized management system, which implements achatbox interface. Also, once a particular project (e.g., widget, formor application) is translated, it can be saved in a repository, e.g.,database, such as the storage system 1022B. In this way, the text fieldof any widget, form or application will not have to be translated again,with the system and method described, through the mapping function,having the ability to traverse the translation throughout themarketplace for similar forms, widgets and applications.

FIG. 12B shows an architecture of the system and method of thetranslation application in accordance with aspects of the presentdisclosure. More specifically, FIG. 12B shows the implementation of theprocessing steps of FIG. 12A within an architectural structure. Inembodiments, the processing steps are implemented by differentcomponents/services within the architecture.

The architecture includes a development tool 12100 and the centralizedmanagement system (Kony IQ™ by Kony, Inc.) 12105. In embodiments, thedevelopment tool 12100 can be e.g., Kony Visualizer® by Kony, Inc. Thecentralized management system (Kony IQ™ by Kony, Inc.) 12105 includesseveral components/services, including: a translation engine 12105 a, acanvas user interface reader 12105 b, a JavaScript code reader 12105 c,a Viz i18n reader 12105 d, a text translator 12105 e and a widgetproperties metadata 12105 f. In implementation of the architecture, eachof the components/services 12105 a-12105 d communicate with thetranslation engine 12105 a and are configured to implement the stepsshown in FIG. 12A. The development tool 12100 includes the followingcomponents/service: canvas user interface forms, templates, etc., 12100a, JavaScript code files 12100 b and an i18n map 12100 c. Inembodiments, the components 12105 a-12105 c communicate with therespective components 12100 b-12100 d of the development tool 12100 whenimplementing steps 12005-12030, 12040 and 12045.

In embodiments, the translation engine 12105 a can live within a networkcloud or within the centralized management system (Kony IQ™ by Kony,Inc.) 12105. In any scenario, the translation engine 12105 a is used toimplement the respective components/services 12105 b-12105 f. The userinterface reader 12105 b passes information between the canvas userinterface forms, templates, etc., 12100 a and the translation engine12105 a, while implementing the steps 12010 and 12015 of FIG. 12A. Byway of example, the canvas user interface reader 12105 b can interfacewith the canvas user interface forms, templates forms and templatesmodule/service 12100 a. The canvas user interface reader 12105 b can bea piece of executable code, which interacts with elements that arespecified at the IDE level. For example, the canvas user interface formsand templates 12105 b are services described and created at thevisualizer IDE level. The canvas user interface reader 12105 b is aservice layer underneath the covers that interoperates with thosefeatures of the integrated development environment. It can, for example,match the world of drag-and-drop user interface with things that wouldmanually either hover over click or be input via keyboard etc., with theworld of the canvas user interface (UI) reader that actually performsthe biological transformations.

Still referring to FIG. 12B, the JavaScript code reader 12105 c canembed the JavaScript AST, e.g., abstract syntax tree interpreter. Inthis way, the JavaScript code reader 12105 c is capable of reading theJavaScript code for the purposes of translation as discussed withrespect to steps 12020 and 12025 of FIG. 12A. For example, the ASTprovides a complete manifest of what is needed to change fortranslation, so that it can be mapped and provided as a collection ofservice calls to the translate service, e.g., translate engine 12105 a.

The viz i18n reader 12105 d places the generated keys to the map 12100 cof the development tool 12100. In embodiments, the viz i18n reader 12105d implements/passes along the information provided in steps 12030, 12040and 12045 of FIG. 12A. The text translator 12105 e is an API wrapperthat essentially is the go-between between the Kony Visualizer® by Kony,Inc (development tool) and the translation API, e.g., implements thestep 12035 of FIG. 12A.

The widget properties metadata component 12105 e, on the other hand, issensitive to the internal structure of metadata that comprises theformat and syntax of the forms, widgets and applications. That is, thewidget properties metadata component 12105 e knows the properties of thewidgets, forms and applications as described with respect to step 12005of FIG. 12A. In embodiments, the widget properties metadata component12105 e can collect all widget details and create a meta with all theproperties of widgets that need to be translated. By way of illustrativeexample, Table 1 below shows the widgets and their properties thatrender text on the user interface.

TABLE 1 Widget Property Form/Form2 title Button text Link text Labeltext Phone text Segment/Segment0 1. pushToRefresh 2. pullToRefresh 3.releaseToPushRefresh 4. releaseToPullRefresh 5. data without sections 6.data with sections Switch 1. leftSideText 2. rightSideText ScrollBox 1.pushToRefresh 2. pullToRefresh 3. releaseToPushRefresh 4.releaseToPullRefresh TextArea/TextArea2 1. text 2. placeholderTextField/TextField2 1. text 2. placeholder Camera text Tab tabNameRichText text Video text Radio group data Checkbox data Combo box dataType Details Templates Widgets inside templates of different typesComponents Widgets inside components Forms Widgets inside form App menuApp menu data Segment data in components Segment inside Component willhave data for each channel Actions “Set widget property” action

FIG. 12C shows screenshots before and after translation conversionsupport as implemented by the system and method of translationapplication. In this example, the conversion is from English toMandarin; although these screenshots are equally applicable to othertranslations. In embodiments, the screenshot 12200 a shows Englishstrings in an i18n map; whereas, screenshot 12200 b shows Mandarinstrings in an i18n map.

FIG. 12D shows screenshots of translation of strings on forms (fromEnglish to Mandarin) as implemented by the system and method oftranslation application. In this example, the conversion is from Englishto Mandarin; although these screenshots are equally applicable to othertranslations. In embodiments, the screenshot 12205 a shows a form inEnglish; whereas, screenshot 12205 b shows the form translated intoMandarin. Accordingly, it is shown that the system and method describedherein, e.g., translation application, can provide translations of auser's interface in response to a question, for example, or any textembedded in the application of the form or the widget itself so theentirety of a widget can be translated.

Referring still to FIG. 12D, the system and method described herein,e.g., translation application, can handle static questions and answersas well as providing the ability to translate web services intodifferent languages. The translation application also has the ability totranslate a portion of the form or any static answer and, at run time ofthe application, bring in the translation and merge it with othercontent coming back from the service call, i.e., a person's name. Forexample, using the information shown in FIG. 12D, the person's name,e.g., John, can be merged with the remaining portion of the answer. Thisis beneficial since the name can be deemed static text which will likelybe input into the target language, e.g., their own native language, sothere should be no need to translate this portion of the text. Instead,it would only be necessary to translate the hard-coded response, e.g.,all other text.

FIG. 12E shows screenshots of a translation of fillable fields andstatic text on forms (from English to Mandarin) as implemented by thesystem and method of translation application. In this example, theconversion is from English to Mandarin; although these screenshots areequally applicable to other translations. In embodiments, the screenshot12210 a shows the form in English (both static and fillable fields);whereas, screenshot 12210 b shows the form translated into Mandarin. Inthis and other examples described herein, the fillable fields can bestatic text hardcoded on the form or can be obtained from a back endservice essentially living in a service call or within a database frontended by a service call that needs to be traversed and translated priorto being populated into the form. This latter feature can be performedthrough the marketplace, for example, using mapping features to find thelocation of the particular text requiring a translation.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a computing device of a cloud-based campaign system, acampaign for products or services from a marketing system via a network;providing, by the computing device, the campaign to one or moreparticipants on the network; obtaining, by the computing device,feedback from the one or more participants regarding the campaign;analyzing, by an artificial intelligence tool of the computing device,the feedback to generate marketing analytics information; and providing,by the computing device, the marketing analytics information to themarketing system via the network.
 2. The computer-implemented method ofclaim 1, wherein the marketing analytics information comprises one ormore selected from the group consisting of: effectiveness of thecampaign; whether the campaign is targeting the correct participantswith the most pertinent products; and whether the campaign is targetingthe correct participants with the most pertinent services.
 3. Thecomputer-implemented method of claim 1, further comprising determining,by the computing device, the one or more participants have opted in to amarketing environment, wherein the providing the campaign to theparticipant is based on the determining that the one or moreparticipants have opted in to the marketing environment.
 4. Thecomputer-implemented method of claim 1, further comprising providing, bythe computing device, the one or more participants with questionnaires,wherein the feedback from the participant is a response to thequestionnaires.
 5. The computer-implemented method of claim 1, furthercomprising determining, by the computing device, that the one or moreparticipants have opted in to receiving notifications from the marketingsystem.
 6. The computer-implemented method of claim 5, furthercomprising receiving by the computing device, a notification to the oneor more participants from the marketing system.
 7. Thecomputer-implemented method of claim 6, further comprising providing, bythe computing device, the notification to the one or more participantsbased on the determining that the one or more participants have opted into receiving notification from the marketing system.
 8. Thecomputer-implemented method of claim 7, further comprising obtaining, bythe computing device, feedback from the one or more participantsregarding the notification.
 9. The computer-implemented method of claim8, wherein the analyzing the feedback from the one or more participantsfurther comprises analyzing the feedback from the one or moreparticipants regarding the notification to generate the marketinganalytics information.
 10. The computer-implemented method of claim 1,wherein the campaign system comprises plug-in architecture.
 11. Acomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith, the program instructionsexecutable by a computing device to cause the computing device to:determine that one or more participants in a marketing network haveopted to receive campaigns for products or services; receive a campaignfor products or services from a marketing system in the marketingnetwork; provide the campaign to the one or more participants on themarketing network based on the determining that the one or moreparticipants in the marketing network opted to receive campaigns forproducts or services; obtain feedback from the one or more participantsregarding the campaign; analyze, by an artificial intelligence tool ofthe computing device, the feedback to generate marketing analyticsinformation; and provide the marketing analytics information to themarketing system.
 12. The computer program product of claim 11, whereinthe marketing analytics information comprises one or more selected fromthe group consisting of: effectiveness of the campaign; whether thecampaign is targeting the correct participants with the most pertinentproducts; and whether the campaign is targeting the correct participantswith the most pertinent services.
 13. The computer program product ofclaim 11, wherein the program instructions further cause the computingdevice to provide the one or more participants with uniquequestionnaires, wherein the feedback from the one or more participantsare responses to the respective unique questionnaires.
 14. The computerprogram product of claim 11, wherein the program instructions furthercause the computing device to determine that the one or moreparticipants have opted in to receiving notifications from the marketingsystem.
 15. The computer program product of claim 14, wherein theprogram instructions further cause the computing device to receive anotification to the one or more participants from the marketing system.16. The computer program product of claim 15, wherein the programinstructions further cause the computing device to provide thenotification to the one or more participants based on the determiningthat the one or more participants have opted in to receivingnotifications from the marketing system.
 17. The computer programproduct of claim 16, wherein the program instructions further cause thecomputing device to obtain feedback from the one or more participantsregarding the notification, wherein the analyzing the feedback from theone or more participants further comprises analyzing the feedback fromthe one or more participants regarding the notification to generate themarketing analytics information.
 18. A campaign system comprising: aprocessor, a computer readable memory and a computer readable storagemedium associated with a computing device; program instructions todetermine that one or more participants in a marketing network haveopted to receive notifications for products or services and/or campaignsfor products or services; program instructions to receive a notificationfor products or services and/or a campaign for products or services froma marketing system in the marketing network; program instructions toprovide the notification and/or the campaign to the one or moreparticipants on the marketing network based on the determining that theone or more participants in the marketing network opted to receive thenotifications and/or the campaigns; program instructions to obtainfeedback from the one or more participants regarding the notificationand/or the campaign; program instructions to analyze, by an artificialintelligence tool of the computing device, the feedback to generatemarketing analytics information; and program instructions to provide themarketing analytics information to the marketing system, wherein theprogram instructions are stored on the computer readable storage mediumfor execution by the processor via the computer readable memory.
 19. Thesystem of claim 18, wherein the marketing analytics informationcomprises one or more selected from the group consisting of:effectiveness of the campaign; whether the campaign is targeting thecorrect participants with the most pertinent products; and whether thecampaign is targeting the correct participants with the most pertinentservices.
 20. The system of claim 18, further comprising programinstruction to provide the one or more participants with uniquequestionnaires, wherein the feedback from the one or more participantsare responses to the respective unique questionnaires.